A Tour of Machine Learning Algorithms | Read this introductory list of machine learning algorithms

Admond Lee
Dec 17, 2020

Contents Outline

A Tour of Machine Learning Algorithms | Read this introductory list of machine learning algorithms

Dec 17, 2020 11 minutes read


An overview of Machine Learning Algorithms(Source)

“Machine intelligence is the last invention that humanity will ever need to make.”
— Nick Bostrom.

If you could look back a couple of years ago at the state of AI and compare it with its current state, you would be shocked to find how exponentially it has grown over time.

It has branched out into a variety of domains such as ML, Expert Systems, NLP, and dozens more.

While the idea behind AI is to build smarter systems that think and execute on their own, they still need to be trained.

The ML domain of AI has been created for the very exact purpose by bringing several algorithms, allowing for smoother data processing and decision-making.

What is Machine Learning Algorithms?


ML algorithms are the brains behind any model, allowing machines to learn, making them smarter.

The way these algorithms work is, they’re provided with an initial batch of data, and with time, as algorithms develop their accuracy, additional data is introduced into the mix.

This process of regularly exposing the algorithm to new data and experience improves the overall efficiency of the machine.

ML algorithms are vital for a variety of tasks related to classification, predictive modeling, and analysis of data.

“A breakthrough in machine learning would be worth ten Microsofts.”
- Bill Gates

Types of Machine Learning Algorithms


In this section, we will focus on the various types of ML algorithms that exist. The three primary paradigms of ML algorithms are:

● Supervised Learning

As the name suggests, Supervised algorithms work by defining a set of input data and the expected results. By iteratively executing the function on the training data and involving the user for introducing control parameters, the model is improved. The algorithm is considered a success when its mappings and predictions are found to be correct.


Supervised Learning(source)

● Unsupervised Learning

While Supervised algorithms work on user-labeled data for output predictions, these train machines explicitly on unlabelled data with little to no user involvement.

Algorithms are left with the data to classify and group them to identify some hidden or undiscovered pattern and is often used as a preliminary step for supervised learning.

Unsupervised Learning(source)

● Reinforcement Learning

Reinforcement learning algorithms aim to find a perfect balance between exploration and exploitation without requiring labeled data or user intervention.

These algorithms work by choosing an action and observing the consequences, based on that, it learns how optimal the result is. This process is repeated time and again until the algorithm evolves and chooses the right strategy.

Top Machine Learning Algorithms


After familiarizing yourself with the several types of ML algorithms, read on for some of the popular ones.

1. Linear Regression

Linear Regression is a supervised ML algorithm that helps find a suitable approximate linear fit to a collection of points.

At its core, linear regression is a linear approach to identifying the relationship between two variables with one of these values being a dependent value and the other being independent.

The idea behind this is to understand how a change in one variable impacts the other, resulting in a relationship that can be positive or negative.

Linear Regression is represented as a line in the form of y = a + bx(source)

This line is known as the regression line and represented by a linear equation Y= a *X + b.

In this equation:
  • Y — Dependent Variable
  • a — Slope
  • X — Independent variable
  • b — Intercept

This algorithm applied in cases where the predicted output is continuous and has a constant slope, such as:
  • Estimating sales
  • Assessing risk
  • Weather data analysis
  • Predictive Analytics
  • Customer survey results analysis
  • Optimizing product prices

Useful links:

2. Logistic Regression

Logistic Regression algorithm is often used in the binary classification problems where the events in these cases commonly result in either of the two values, pass or fail, true or false.

It is best suited for situations where there is a need to predict probabilities that the dependent variable will fall into one of the two categories of the response.

Common use cases for this algorithm would be to identify whether the given handwriting matches to the person in question, will the prices of oil go up in coming months.

Logistic Regression algorithm(source)

In general, regressions can be used in real-world applications such as:
  • Credit Scoring
  • Cancer Detection
  • Geographic Image Processing
  • Handwriting recognition
  • Image Segmentation and Categorization
  • Measuring the success rates of marketing campaigns
  • Predicting the revenues of a certain product
  • Is there going to be an earthquake on a particular day?

Useful links:

3. Decision Trees

Decision Tree algorithm comes under supervised ML and is used for solving regression and classification problems. The purpose is to use a decision tree to go from observations to processing outcomes at each level.

Processing decision trees is a top-down approach where the best suitable attribute from the training data is selected as the root and, the process is repeated for each branch. Decision Trees are commonly used for:
  • Building knowledge management platforms
  • Selecting a flight to travel
  • Predicting high occupancy dates for hotels
  • Suggest a customer what car to buy
  • Forecasting predictions and identifying possibilities in various domains

Decision Tree algorithm(source)

Useful links:

4. Apriori Machine Learning Algorithm

It is the algorithm behind the popular recommendation system called “You may also like” on several online platforms.

It operates by searching for common sets of items in the datasets and later builds associations on them.

It is generally used for itemset mining and association rule learning from relational databases.

The idea behind this algorithm is to keep extending related items to a larger set as possible to create a more useful association.

Applications for this algorithm include highlighting buying trends in the market.

Moreover, it is easier to implement and can be used with large datasets.

5. Naive Bayes

Naive Bayes classifiers are categorized as a highly effective supervised ML algorithm and are one of the simplestBayesian Network models.

It works by applying the Bayes’ theorem on data with a naive assumption of conditional independence between every pair of features, given the value of the class variable.


Naive Bayes(source)

In simpler terms, it helps find the probability of an event A happening, given that event B has occurred. Naive Bayes is best for —
  • Filtering spam messages
  • Recommendation systems such as Netflix
  • Classify a news article about technology, politics, or sports
  • Sentiment analysis on social media
  • Facial recognition software

6. Artificial Neural Networks

Modeled after the human brain, the Artificial Neural Network acts as an enormous labyrinth of neurons or simply, nodes moving information to and from each other.

These interconnected nodes pass data instantaneously to other nodes via the edges for swift processing, facilitating smoother learning.

ANNs learn with examples, instead of being programmed with a specific set of rules. Able to model nonlinear processes, they can be implemented in areas such as —
  • Pattern recognition
  • Cybersecurity
  • Data mining
  • Detecting varieties of cancer in patients

Artificial Neural Networks(source)

7. K-means Clustering

k-means clustering is an iterative unsupervised learning algorithm that partitions n observations into k clusters where each observation belongs to the nearest cluster mean.


Steps of the K-means algorithm(source)

In simpler terms, this algorithm aggregates a collection of data points based on their similarity. Its applications range from clustering similar and relevant web search results, in programming languages and libraries such as Python, SciPy, Sci-Kit Learn, and data mining.

Real-World applications of K-means Clustering —
  1. Identifying fake news
  2. Spam detection and filtering
  3. Classify books or movies by genre
  4. Popular transport routes while town planning

Useful links:

8. Support Vector Machines

Support Vector Machines are categorized as supervised machine learning algorithms and are primarily used for classification and regression analysis.

The algorithm works by building models that assign new examples and data to a category, where these categories are easily distinguishable from one another by a gap.

SVM is highly effective in cases where the number of dimensions outweighs the number of samples and is extremely memory-efficient.

Highly effective Support Vector Machines algorithm(source)

SVM applications can be found in:
  • Face Detection
  • Image Classification
  • Text & Hypertext Categorization
  • Handwriting recognition
  • Drug Discovery for Therapy
  • Bioinformatics — protein, genes, biological, or cancer classification.

Useful links:

9. K-nearest Neighbors

K-nearest neighbors is a supervised ML algorithm used for both regression and classification problems.

Usually implemented for pattern recognition, this algorithm first stores, and identifies the distance between all inputs in the data using a distance function, selects the k specified inputs closest to query and outputs:
  • The most frequent label (for classification)
  • The average value of k nearest neighbors (for regression)

K-nearest Neighbors(source)

Real-life applications of this algorithm include —
  • Fingerprint detection
  • Credit rating
  • Forecasting the stock market
  • Analyzing money laundering
  • Bank bankruptcies
  • Currency exchange rate

10. Dimensionality Reduction Algorithms

Dimensionality Reduction algorithms work by reducing the dimension space or the number of random variables in a dataset by using one of the two primary approaches, Feature Selection or Feature Extraction.

These are often applied to preprocess the datasets, and to remove redundant features, making it easier for algorithms to train the model.

These algorithms also come with a few nifty benefits, such as:
  • Low storage requirements
  • Less computing power required
  • Increased accuracy
  • Reduced noise

A few well-known Dimensionality Reduction algorithms are:
  • Principal Component Analysis
  • Linear Discriminant Analysis
  • Locally Linear Embedding
  • Multi-dimensional Scaling

11. Principal Component Analysis

Principal Component Analysis is one of the unsupervised algorithms for ML and is primarily used for reducing dimensions of your feature space by using either Feature Elimination or Feature Extraction.

It is also used as a tool for exploratory data analysis and building predictive models. Requiring normalized data, PCA can help with:
  • Image Processing
  • Movie recommendation system
  • Calculating data covariance matrix
  • Perform eigenvalue decomposition on the covariance matrix
  • Optimize power allocation in multiple communication channels

Principal Component Analysis(source)

PCA aims to reduce redundancies from the datasets, making it simpler without compromising on accuracy. It is commonly deployed in image processing and risk management sectors.

Useful links:

12. Random Forests

Random Forests use a variety of algorithms for solving classification, regression, and similar problems by implementing decision trees.

The way it works is, it creates heaps of decision trees with random sets of data, and a model is trained repeatedly on it for near-accurate results.

In the end, all the results from these decision trees are combined to identify the best suitable result that appears most commonly in the output.


Random forests(source)

Random Forests applications can be found in —
  1. Fraud detection for bank accounts, credit card
  2. Detect and predict the drug sensitivity of a medicine
  3. Identify a patient’s disease by analyzing their medical records
  4. Predict estimated loss or profit while purchasing a particular stock

13. Gradient Boosting & AdaBoost

Boosting is a technique for ensemble ML algorithms converting weak learners to strong learners. Boosting algorithms are required when data is abundant, and we seek to reduce the bias and variance in supervised learning. Below are two of the popular boosting algorithms.
  • Gradient Boosting

Gradient Boosting algorithm is used for classification and regression problems by building a prediction model typically in an iterative manner such as the decision trees. It improves the weak learners by training it on the errors of the strong learners resulting in an overall accurate learner.
  • AdaBoost

Short for Adaptive Boosting, AdaBoost improves the model when the weak learners fail. It does so by modifying the weights attached to the instances in the sample to focus more on the hard ones, later, the output from the weak learners is combined to form a weighted sum, and is considered the final boosted output.


Conclusion

ML algorithms are vital for Data Scientists due to their increasing applications in the real world. With a variety of algorithms mentioned above, you can find an algorithm that best solves your problem. These algorithms, albeit being a mix of supervised and unsupervised, can handle a variety of tasks and are capable of working in sync with other algorithms.



Further Sources —

I hope you’ve found this article useful! Below are additional resources if you’re interested in learning more: —


About Author
Claire D. is a Content Crafter and Marketer at Digitalogy — a tech sourcing and custom matchmaking marketplace that connects people with pre-screened & top-notch developers and designers based on their specific needs across the globe. Connect with Digitalogy on Linkedin, Twitter, Instagram.

Related Posts

Model Evaluation Metrics in Machine Learning

DataSource AI Hosts KTM AG's Inaugural AI Challenge: "Code the Light Fantastic"

The Role of AI in Unstructured Data Mining: Challenges and Opportunities

Categories

Data Science Machine Learning Deep learning Flask Deployment Big Data Business Python SQL Programming Libraries Jupyter Numpy News Pandas Matplotlib Visualization

Join Competition

Flight Delay Prediction Competition

Keyword Recency Prediction

Most Related Articles

Data Science
Machine Learning
Model Evaluation Metrics in Machine Learning

CreditsPredictive models have become a trusted advisor to many businesses and for a good reason. These models can “foresee the future”, and there are many different methods available, meaning any industry can find one that fits their particular challenges.When we talk about predictive models, we are talking either about a regression model (continuous output) or a classification model (nominal or binary output). In classification problems, we use two types of algorithms (dependent on the kind of output it creates):Class output: Algorithms like SVM and KNN create a class output. For instance, in a binary classification problem, the outputs will be either 0 or 1. However, today we have algorithms that can convert these class outputs to probability.Probability output: Algorithms like Logistic Regression, Random Forest, Gradient Boosting, Adaboost, etc. give probability outputs. Converting probability outputs to class output is just a matter of creating a threshold probability.IntroductionWhile data preparation and training a machine learning model is a key step in the machine learning pipeline, it’s equally important to measure the performance of this trained model. How well the model generalizes on the unseen data is what defines adaptive vs non-adaptive machine learning models.By using different metrics for performance evaluation, we should be in a position to improve the overall predictive power of our model before we roll it out for production on unseen data.Without doing a proper evaluation of the ML model using different metrics, and depending only on accuracy, it can lead to a problem when the respective model is deployed on unseen data and can result in poor predictions.This happens because, in cases like these, our models don’t learn but instead memorize;hence, they cannot generalize well on unseen data.Model Evaluation MetricsLet us now define the evaluation metrics for evaluating the performance of a machine learning model, which is an integral component of any data science project. It aims to estimate the generalization accuracy of a model on the future (unseen/out-of-sample) data.Confusion MatrixA confusion matrix is a matrix representation of the prediction results of any binary testing that is often used to describe the performance of the classification model (or “classifier”) on a set of test data for which the true values are known.The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.Confusion matrix with 2 class labels.Each prediction can be one of the four outcomes, based on how it matches up to the actual value:True Positive (TP): Predicted True and True in reality.True Negative (TN): Predicted False and False in reality.False Positive (FP): Predicted True and False in reality.False Negative (FN): Predicted False and True in reality.Now let us understand this concept using hypothesis testing.A Hypothesis is speculation or theory based on insufficient evidence that lends itself to further testing and experimentation. With further testing, a hypothesis can usually be proven true or false.A Null Hypothesis is a hypothesis that says there is no statistical significance between the two variables in the hypothesis. It is the hypothesis that the researcher is trying to disprove.We would always reject the null hypothesis when it is false, and we would accept the null hypothesis when it is indeed true.Even though hypothesis tests are meant to be reliable, there are two types of errors that can occur.These errors are known as Type 1 and Type II errors.For example, when examining the effectiveness of a drug, the null hypothesis would be that the drug does not affect a disease.Type I Error:- equivalent to False Positives(FP).The first kind of error that is possible involves the rejection of a null hypothesis that is true.Let’s go back to the example of a drug being used to treat a disease. If we reject the null hypothesis in this situation, then we claim that the drug does have some effect on a disease. But if the null hypothesis is true, then, in reality, the drug does not combat the disease at all. The drug is falsely claimed to have a positive effect on a disease.Type II Error:- equivalent to False Negatives(FN).The other kind of error that occurs when we accept a false null hypothesis. This sort of error is called a type II error and is also referred to as an error of the second kind.If we think back again to the scenario in which we are testing a drug, what would a type II error look like? A type II error would occur if we accepted that the drug hs no effect on disease, but in reality, it did.A sample python implementation of the Confusion matrix.import warnings import pandas as pd from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import confusion_matrix import matplotlib.pyplot as plt %matplotlib inline   #ignore warnings warnings.filterwarnings('ignore') # Load digits dataset url = "http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data" df = pd.read_csv(url) # df = df.values X = df.iloc[:,0:4] y = df.iloc[:,4] #test size test_size = 0.33 #generate the same set of random numbers seed = 7 #Split data into train and test set. X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) #Train Model model = LogisticRegression() model.fit(X_train, y_train) pred = model.predict(X_test)  #Construct the Confusion Matrix labels = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'] cm = confusion_matrix(y_test, pred, labels) print(cm) fig = plt.figure() ax = fig.add_subplot(111) cax = ax.matshow(cm) plt.title('Confusion matrix') fig.colorbar(cax) ax.set_xticklabels([''] + labels) ax.set_yticklabels([''] + labels) plt.xlabel('Predicted Values') plt.ylabel('Actual Values') plt.show()Confusion matrix with 3 class labels.The diagonal elements represent the number of points for which the predicted label is equal to the true label, while anything off the diagonal was mislabeled by the classifier. Therefore, the higher the diagonal values of the confusion matrix the better, indicating many correct predictions.In our case, the classifier predicted all the 13 setosa and 18 virginica plants in the test data perfectly. However, it incorrectly classified 4 of the versicolor plants as virginica.There is also a list of rates that are often computed from a confusion matrix for a binary classifier:1. AccuracyOverall, how often is the classifier correct?Accuracy = (TP+TN)/totalWhen our classes are roughly equal in size, we can use accuracy, which will give us correctly classified values.Accuracy is a common evaluation metric for classification problems. It’s the number of correct predictions made as a ratio of all predictions made.Misclassification Rate(Error Rate): Overall, how often is it wrong. Since accuracy is the percent we correctly classified (success rate), it follows that our error rate (the percentage we got wrong) can be calculated as follows:Misclassification Rate = (FP+FN)/totalWe use the sklearn module to compute the accuracy of a classification task, as shown below.#import modules import warnings import pandas as pd import numpy as np from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn import datasets from sklearn.metrics import accuracy_score #ignore warnings warnings.filterwarnings('ignore') # Load digits dataset iris = datasets.load_iris() # # Create feature matrix X = iris.data # Create target vector y = iris.target #test size test_size = 0.33 #generate the same set of random numbers seed = 7 #cross-validation settings kfold = model_selection.KFold(n_splits=10, random_state=seed) #Model instance model = LogisticRegression() #Evaluate model performance scoring = 'accuracy' results = model_selection.cross_val_score(model, X, y, cv=kfold, scoring=scoring) print('Accuracy -val set: %.2f%% (%.2f)' % (results.mean()*100, results.std()))  #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) #fit model model.fit(X_train, y_train) #accuracy on test set result = model.score(X_test, y_test) print("Accuracy - test set: %.2f%%" % (result*100.0))The classification accuracy is 88% on the validation set.2. PrecisionWhen it predicts yes, how often is it correct?Precision=TP/predicted yesWhen we have a class imbalance, accuracy can become an unreliable metric for measuring our performance. For instance, if we had a 99/1 split between two classes, A and B, where the rare event, B, is our positive class, we could build a model that was 99% accurate by just saying everything belonged to class A. Clearly, we shouldn’t bother building a model if it doesn’t do anything to identify class B; thus, we need different metrics that will discourage this behavior. For this, we use precision and recall instead of accuracy.3. Recall or SensitivityWhen it’s actually yes, how often does it predict yes?True Positive Rate = TP/actual yesRecall gives us the true positive rate (TPR), which is the ratio of true positives to everything positive.In the case of the 99/1 split between classes A and B, the model that classifies everything as A would have a recall of 0% for the positive class, B (precision would be undefined — 0/0). Precision and recall provide a better way of evaluating model performance in the face of a class imbalance. They will correctly tell us that the model has little value for our use case.Just like accuracy, both precision and recall are easy to compute and understand but require thresholds. Besides, precision and recall only consider half of the confusion matrix:4. F1 ScoreThe F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.Why harmonic mean? Since the harmonic mean of a list of numbers skews strongly toward the least elements of the list, it tends (compared to the arithmetic mean) to mitigate the impact of large outliers and aggravate the impact of small ones.An F1 score punishes extreme values more. Ideally, an F1 Score could be an effective evaluation metric in the following classification scenarios:When FP and FN are equally costly — meaning they miss on true positives or find false positives — both impact the model almost the same way, as in our cancer detection classification exampleAdding more data doesn’t effectively change the outcome effectivelyTN is high (like with flood predictions, cancer predictions, etc.)A sample python implementation of the F1 score.import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import log_loss from sklearn.metrics import precision_recall_fscore_support as score, precision_score, recall_score, f1_score  warnings.filterwarnings('ignore')  url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] test_size = 0.33 seed = 7  model = LogisticRegression() #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train) precision = precision_score(y_test, pred) print('Precision: %f' % precision) # recall: tp / (tp + fn) recall = recall_score(y_test, pred) print('Recall: %f' % recall) # f1: tp / (tp + fp + fn) f1 = f1_score(y_test, pred) print('F1 score: %f' % f1)5. SpecificityWhen it’s no, how often does it predict no?True Negative Rate=TN/actual noIt is the true negative rate or the proportion of true negatives to everything that should have been classified as negative.Note that, together, specificity and sensitivity consider the full confusion matrix:6. Receiver Operating Characteristics (ROC) CurveMeasuring the area under the ROC curve is also a very useful method for evaluating a model. By plotting the true positive rate (sensitivity) versus the false-positive rate (1 — specificity), we get the Receiver Operating Characteristic (ROC) curve. This curve allows us to visualize the trade-off between the true positive rate and the false positive rate.The following are examples of good ROC curves. The dashed line would be random guessing (no predictive value) and is used as a baseline; anything below that is considered worse than guessing. We want to be toward the top-left corner:A sample python implementation of the ROC curves.#Classification Area under curve import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import roc_auc_score, roc_curve  warnings.filterwarnings('ignore')  url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] seed = 7 #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train)  # predict probabilities probs = model.predict_proba(X_test) # keep probabilities for the positive outcome only probs = probs[:, 1]  auc = roc_auc_score(y_test, probs) print('AUC - Test Set: %.2f%%' % (auc*100))  # calculate roc curve fpr, tpr, thresholds = roc_curve(y_test, probs) # plot no skill plt.plot([0, 1], [0, 1], linestyle='--') # plot the roc curve for the model plt.plot(fpr, tpr, marker='.') plt.xlabel('False positive rate') plt.ylabel('Sensitivity/ Recall') # show the plot plt.show()In the example above, the AUC is relatively close to 1 and greater than 0.5. A perfect classifier will have the ROC curve go along the Y-axis and then along the X-axisLog LossLog Loss is the most important classification metric based on probabilities.As the predicted probability of the true class gets closer to zero, the loss increases exponentially:It measures the performance of a classification model where the prediction input is a probability value between 0 and 1. Log loss increases as the predicted probability diverge from the actual label. The goal of any machine learning model is to minimize this value. As such, smaller log loss is better, with a perfect model having a log loss of 0.A sample python implementation of the Log Loss.#Classification LogLoss import warnings import pandas from sklearn import model_selection from sklearn.linear_model import LogisticRegression from sklearn.metrics import log_loss  warnings.filterwarnings('ignore') url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv" dataframe = pandas.read_csv(url) dat = dataframe.values X = dat[:,:-1] y = dat[:,-1] seed = 7 #split data X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=test_size, random_state=seed) model.fit(X_train, y_train) #predict and compute logloss pred = model.predict(X_test) accuracy = log_loss(y_test, pred) print("Logloss: %.2f" % (accuracy))Logloss: 8.02 Jaccard IndexJaccard Index is one of the simplest ways to calculate and find out the accuracy of a classification ML model. Let’s understand it with an example. Suppose we have a labeled test set, with labels as –y = [0,0,0,0,0,1,1,1,1,1]And our model has predicted the labels as –y1 = [1,1,0,0,0,1,1,1,1,1]The above Venn diagram shows us the labels of the test set and the labels of the predictions, and their intersection and union.Jaccard Index or Jaccard similarity coefficient is a statistic used in understanding the similarities between sample sets. The measurement emphasizes the similarity between finite sample sets and is formally defined as the size of the intersection divided by the size of the union of the two labeled sets, with formula as –Jaccard Index or Intersection over Union(IoU)So, for our example, we can see that the intersection of the two sets is equal to 8 (since eight values are predicted correctly) and the union is 10 + 10–8 = 12. So, the Jaccard index gives us the accuracy as –So, the accuracy of our model, according to Jaccard Index, becomes 0.66, or 66%.Higher the Jaccard index higher the accuracy of the classifier.A sample python implementation of the Jaccard index.import numpy as np def compute_jaccard_similarity_score(x, y):     intersection_cardinality = len(set(x).intersection(set(y)))     union_cardinality = len(set(x).union(set(y)))     return intersection_cardinality / float(union_cardinality)  score = compute_jaccard_similarity_score(np.array([0, 1, 2, 5, 6]), np.array([0, 2, 3, 5, 7, 9])) print "Jaccard Similarity Score : %s" %score passJaccard Similarity Score : 0.375Kolomogorov Smirnov chartK-S or Kolmogorov-Smirnov chart measures the performance of classification models. More accurately, K-S is a measure of the degree of separation between positive and negative distributions.The cumulative frequency for the observed and hypothesized distributions is plotted against the ordered frequencies. The vertical double arrow indicates the maximal vertical difference.The K-S is 100 if the scores partition the population into two separate groups in which one group contains all the positives and the other all the negatives. On the other hand, If the model cannot differentiate between positives and negatives, then it is as if the model selects cases randomly from the population. The K-S would be 0.In most classification models the K-S will fall between 0 and 100, and that the higher the value the better the model is at separating the positive from negative cases.The K-S may also be used to test whether two underlying one-dimensional probability distributions differ. It is a very efficient way to determine if two samples are significantly different from each other.A sample python implementation of the Kolmogorov-Smirnov.from scipy.stats import kstest import random   # N = int(input("Enter number of random numbers: ")) N = 10   actual =[] print("Enter outcomes: ")   for i in range(N):     # x = float(input("Outcomes of class "+str(i + 1)+": "))     actual.append(random.random())   print(actual) x = kstest(actual, "norm")    print(x)The Null hypothesis used here assumes that the numbers follow the normal distribution. It returns statistics and p-value. If the p-value is < alpha, we reject the Null hypothesis.Alpha is defined as the probability of rejecting the null hypothesis given the null hypothesis(H0) is true. For most of the practical applications, alpha is chosen as 0.05.Gain and Lift ChartGain or Lift is a measure of the effectiveness of a classification model calculated as the ratio between the results obtained with and without the model. Gain and lift charts are visual aids for evaluating the performance of classification models. However, in contrast to the confusion matrix that evaluates models on the whole population gain or lift chart evaluates model performance in a portion of the population.The higher the lift (i.e. the further up it is from the baseline), the better the model.The following gains chart, run on a validation set, shows that with 50% of the data, the model contains 90% of targets, Adding more data adds a negligible increase in the percentage of targets included in the model.Gain/lift chartLift charts are often shown as a cumulative lift chart, which is also known as a gains chart. Therefore, gains charts are sometimes (perhaps confusingly) called “lift charts”, but they are more accurately cumulative lift charts.It is one of their most common uses is in marketing, to decide if a prospective client is worth calling.Gini CoefficientThe Gini coefficient or Gini Index is a popular metric for imbalanced class values. The coefficient ranges from 0 to 1 where 0 represents perfect equality and 1 represents perfect inequality. Here, if the value of an index is higher, then the data will be more dispersed.Gini coefficient can be computed from the area under the ROC curve using the following formula:Gini Coefficient = (2 * ROC_curve) — 1ConclusionUnderstanding how well a machine learning model is going to perform on unseen data is the ultimate purpose behind working with these evaluation metrics. Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced and there’s a class disparity, then other methods like ROC/AUC, Gini coefficient perform better in evaluating the model performance.Well, this concludes this article. I hope you guys have enjoyed reading it, feel free to share your comments/thoughts/feedback in the comment section.Thanks for reading !!!

Data Science
DataSource AI Hosts KTM AG's Inaugural AI Challenge: "Code the Light Fantastic"

DataSource AI announces the launch of the KTM AG inaugural AI Challenge, an unprecedented 3-month online competition that aims to revolutionise two-wheeler innovation through artificial intelligence and deep learning. KTM AG is a global frontrunner in two-wheeler innovation, known for pushing the boundaries of what's possible in the world of motorcycles. With a rich history of groundbreaking engineering and a commitment to cutting-edge technology, KTM AG has set new standards in performance, design, and safety. As a global leader in two-wheeler innovation, KTM AG invites participants to embark on this groundbreaking innovation journey. At the core of this competition lies a challenge set to redefine the future of motorcycle lighting systems. Participants are tasked with developing an algorithm for a high-beam lighting system utilizing a pixel matrix. Participants can find detailed guidelines in the Datathon competition. The datathon unfolds in a 3-tiered cascade model: This Code Challenge by KTM AG promises not only substantial rewards but also an exciting opportunity to shape the future of two-wheeler technology, along with supporting the participants to upscale and test their knowledge in a global AI competition. The cumulative budget for this remarkable Code Challenge by KTM AG is a substantial €24,000, motivating participants with not only the opportunity to push the boundaries of two-wheeler technology but also significant rewards for those who rise to the occasion. With cumulative prizes, contestants have the chance to potentially take home a maximum reward of €10,800 in addition to contributing to cutting-edge advancements in the field.  We invite all aspiring innovators, data scientists, and AI enthusiasts to join us in this journey to "Code the Light Fantastic." For more information, rules, and registration details, please register hereAbout DataSource:  At DataSource AI, we are driven by a singular mission - to democratise the immense power of data science and AI/ML for businesses of all sizes and budgets. We facilitate AI competitions, for businesses of all sizes and budgets by harnessing our extensive data expert community that's collaborating over our intelligent AI algorithm crowdsourcing platform. Our community is at the heart of what we do. We've built a diverse and talented pool of data experts who are passionate about solving real-world problems. They collaborate, ideate, and innovate, driving forward the frontiers of data science.  

Machine Learning
The Role of AI in Unstructured Data Mining: Challenges and Opportunities

In our fast-paced digital world, we're producing staggering volumes of data every day. This data falls into two key categories: structured, known for its order and efficiency, and unstructured, a captivating puzzle brimming with untapped potential.In this article, we will uncover how AI confronts the complexities of unstructured data, the hurdles it faces, and the intriguing opportunities it opens up to businesses from any kind of industry.Understanding Unstructured DataUnstructured data mining is the technique of extracting valuable and meaningful insights from an abundant well of unstructured data. It uncovers hidden gems of knowledge, making it a crucial pursuit in our data-rich era.In today's digital realm, unstructured data is generated in unprecedented quantities. Billions of text documents, images, and videos come to life daily, creating a treasure trove of information just waiting for organizations to explore.Unlocking the insights hidden within unstructured data can provide organizations with a competitive edge. This data can reveal customer sentiments, emerging trends, and valuable feedback that might otherwise go unnoticed.The Basics of Data MiningHow data mining works is that it discovers patterns, trends, and valuable information within a dataset. It involves various techniques to extract knowledge from raw data. While it's exceptionally effective with structured data, applying data mining to unstructured data requires a unique set of skills and tools.Unstructured Data MiningUnstructured data mining is a method focused on the extraction of valuable information from the vast, unstructured data available. This process uncovers hidden insights, making it a valuable endeavor in today's data-driven world.The AI RevolutionThe AI revolution has given rise to an exciting era of possibilities in unstructured data mining. AI's remarkable capabilities are instrumental in taming the unstructured data landscape, and it involves a multitude of components, including:Machine learning enables AI systems to learn from data, make predictions, and identify patterns, enhancing data mining capabilities.Deep learning uses neural networks to model complex patterns in unstructured data, which is particularly valuable in image and speech recognition.Sentiment analysis gauges emotional tones within textual data, helping to understand public opinion and tailor strategies.Pattern recognition identifies recurring structures in data, aiding in image processing and text mining.Knowledge graphs structure data relationships, improving contextual understanding and data retrieval.Anomaly detection identifies outliers in data, which is essential for fraud detection and data security.Challenges in Unstructured Data MiningAs promising as AI is at handling unstructured data, it's not without its set of challenges. Here, we delve into some of the major hurdles:Data QualityUnstructured data is inherently messy. It's laden with errors, inconsistencies, and biases, which makes it a challenge to extract meaningful insights from this data. AI systems need to be trained rigorously to navigate and decipher this diversity in data quality. Techniques like data cleansing, normalization, and the use of context are essential in ensuring that AI systems provide accurate results.ScalabilityAs the volume of unstructured data grows, AI systems must scale to handle the data influx effectively. Traditional hardware and algorithms might not be sufficient to handle this data influx. Scalable infrastructure and distributed computing become crucial to ensuring that AI systems can process and analyze vast amounts of data efficiently.Privacy ConcernsMining unstructured data often raises ethical questions regarding privacy and data protection. That’s why it’s essential to strike the right balance between data utilization and respecting individual privacy. It's a challenge to ensure that AI systems are used responsibly and in compliance with data protection laws and regulations, such as GDPR in Europe. Techniques like anonymization and consent management play a vital role in addressing these privacy concerns.Opportunities and ApplicationsAI's role in unstructured data mining has opened up a world of opportunities across various industries. Let's explore some of the most promising applications:Customer InsightsUnstructured data, particularly sourced from social media and customer reviews, serves as a goldmine of information on customer behavior and preferences. By leveraging AI algorithms, companies can analyze sentiments, spot emerging trends, and even forecast future buying patterns. With these insights, they can fine-tune their marketing strategies, product development, and customer service to align with their ever-evolving audience's demands.Healthcare DiagnosisThe abundance of unstructured data found in medical records, radiological images, and wearable device data holds the key to transformative advancements. AI-powered systems, known for their proficiency in the analysis of this data, not only facilitate early disease detection but also provide highly individualized treatment plans, ultimately raising the standard of patient care. For example: AI expedites the process of analyzing medical images for anomalies, resulting in a significant reduction in the time required for diagnosing and treating severe conditions.Fraud DetectionWhen it comes to financial institutions, AI is a vital tool for exposing fraudulent activities that often hide within the vast volumes of unstructured transaction data. Through a meticulous examination of transaction patterns and anomalies, AI systems can rapidly pinpoint fraudulent actions, providing businesses with a robust defense against significant financial losses. The ability to detect and thwart fraud in real-time provides a critical advantage, resulting in annual savings of billions of dollars for businesses.ConclusionThe future belongs to those who embrace the AI revolution in unstructured data mining. In this future, data isn't just information; it's the key to success. So, let's move forward, embracing this tomorrow, where possibilities are limitless and opportunities are endless.

Data Science
Data-Driven Creativity: Enhancing Video Content through Data Science

In the age of digital marketing and content creation, data-driven creativity is becoming an increasingly important concept. It's the fusion of artistic vision with the insights gleaned from data science to enhance the impact and effectiveness of video content. This 2500-word blog will explore how data science can be leveraged to elevate video content creation, ensuring that it not only engages but also resonates with the intended audience.Introduction to Data-Driven CreativityData-driven creativity marks a groundbreaking shift in video content creation, blending artistic vision with the insights provided by data science. This combination allows creators to break free from conventional creative limits, using data analytics to develop content that is both visually captivating and strategically significant. By delving into viewer behavior, preferences, and interactions, creators can refine their stories and visuals, achieving a deeper connection with their audience. This technique effectively transforms data into a guide for storytelling, steering content towards increased relevance and attractiveness. Consequently, video content becomes a more potent medium for engaging viewers and delivering impactful messages. Fundamentally, data-driven creativity is about converting data points into compelling stories and turning analytical insights into creative masterpieces, thereby redefining the standards of digital video content.Understanding the Role of Data in Video Content CreationExploring the Role of Data in Video Content Creation ventures into the rapidly growing realm of data-driven creativity, where data science emerges as a key instrument in enriching video content. In this realm, data transcends mere figures to become a narrative element, providing rich insights into what audiences prefer, how they behave, and emerging trends. Utilizing data, video creators can break free from conventional creative constraints, shaping their stories to more deeply connect with viewers. This process involves a detailed examination of viewer interactions, demographics, and feedback to hone storytelling skills, aiming to create videos that are not only watched but also emotionally impactful and memorable. Data-driven creativity is a fusion of art and science, where each view, reaction, and comment plays a role in directing the trajectory of video content, enhancing its relevance, engagement, and effect. This marks a transformative phase in content creation, where data equips creators to weave narratives that are not just creatively rich but also finely tuned to the dynamic preferences and interests of their audience.The Process of Gathering and Analyzing DataCollecting and analyzing data forms the foundation of data-driven creativity, especially in the realm of video content enhancement. This process involves the acquisition of key information, including audience demographics, interaction metrics, and performance measures, utilizing sophisticated tools and technologies. These range from social media analytics to advanced data mining applications designed to track a broad spectrum of viewer interactions. Once collected, this data undergoes thorough analysis to identify trends, preferences, and behaviors within the target audience. Such analysis equips content creators with insightful knowledge, allowing them to adjust their video content for greater appeal and connection with their audience. Leveraging these insights, creators can modify elements such as the tone, style, and themes of their content, revolutionizing storytelling methods and ensuring their content is both captivating and impactful. This integration of data science with creative storytelling heralds a transformative phase in video content production, where analytical findings significantly enhance artistic expression.Tailoring Content to Audience PreferencesAdapting content to audience preferences through data-driven creativity signifies a vital evolution in video content production. By incorporating data science, creators gain profound insights into audience behaviors, likes, and engagement patterns. This approach facilitates the creation of content that better resonates with viewers, ensuring everything from the plot to visual elements aligns with their interests. Utilizing analytics such as viewer habits and interaction rates, creators can pinpoint engaging aspects for better video content. Using a high-quality video editor tool is important to make the video look better. This knowledge allows precise adjustments, making the content not only captivating but also highly relevant. Ultimately, incorporating data in video content creation leads to more impactful and resonant viewer experiences, forging a deeper bond between the audience and the content.Enhancing Storytelling with Data InsightsUtilizing data insights to enhance storytelling is a groundbreaking method in video content production. Termed data-driven creativity, this technique blends the storytelling craft with data science accuracy. Content creators leverage analysis of viewer engagement, preferences, and behavior to fine-tune their narratives, ensuring a deeper connection with their audience. This integration results in not only engaging narratives but also ones that are in tune with audience interests and emerging trends. Insights from data grant a clearer understanding of what truly engages viewers, empowering creators to optimize their storytelling for the greatest effect. This modern approach reinvents traditional storytelling into an experience that's both more impactful and centered around the audience, with each creative decision being shaped and enriched by data.Using Data to Predict Future TrendsUtilizing data for future trends in data-driven creativity marks a revolutionary step in improving video content via data science. This technique focuses on analyzing viewer interactions, demographic information, and behavioral tendencies to predict future content direction. Using data enables creators to be proactive, crafting video content that resonates with emerging audience preferences and interests. Such a forward-thinking approach guarantees ongoing relevance in a dynamic digital world and fosters innovation and leadership among content creators. The blend of data analytics and artistic insight leads to the production of not just captivating but also pioneering videos, demonstrating the significant role of data in shaping the future of video content creation.Balancing Creativity and DataAchieving a harmonious blend of creativity and data in video content production is both subtle and potent. Data-driven creativity embodies the convergence of artistic flair and data analytics, providing an innovative method to boost video effectiveness. By weaving in data analysis, video creators unlock insights into what their audience prefers and how they behave, guiding their artistic choices. This integration results in content that is not only enthralling but also deeply meaningful to viewers. It is essential, however, to ensure that data serves as a guide, not a ruler, in the creative journey. This equilibrium keeps the content fresh and appealing while aligning it thoughtfully with data-driven knowledge. In essence, data-driven creativity in video content merges the narrative craft with analytical insights, culminating in videos that are both compelling and influential.Overcoming Challenges in Data-Driven CreativityOvercoming hurdles in data-driven creativity necessitates a nuanced integration of data science into the creation of video content. It involves striking a delicate balance between analytical methodologies and artistic expression, ensuring that data serves as an informative tool rather than a constraint on creativity. Accurate interpretation of data empowers content creators to avoid formulaic outputs, utilizing insights to enrich storytelling and enhance audience engagement. This intricate process demands a comprehensive understanding of the artistry of video creation and the scientific principles behind data analysis. Ethical considerations, including respecting audience privacy and obtaining data consent, are pivotal in this approach. Innovative strategies within data-driven creativity empower creators to produce content that forges deeper connections with viewers, setting new benchmarks in the digital landscape. Embracing these challenges is essential for unlocking the full potential of data-enhanced video content.Ethical Considerations in Data-Driven CreativityIn the domain of data-driven creativity, ethical considerations play a crucial role, especially when utilizing data science to enhance video content. While utilizing data insights can enhance creative processes, it is essential to address privacy concerns and ensure transparent, responsible data usage. Achieving the right equilibrium between creativity and ethical considerations becomes paramount as brands employ data to customize video content. Upholding user privacy and securing informed consent are fundamental principles in ethical data-driven creativity, fostering trust among audiences. Moreover, there is an obligation to avoid perpetuating biases and stereotypes in content creation, championing inclusivity and diversity. Ethical practices not only maintain brand integrity but also contribute to a positive and respectful digital environment for consumers.Tools and Resources for Data-Driven Video CreationExplore the potential of data-driven creativity using state-of-the-art tools and resources for crafting videos. In the current digital landscape, integrating data science and video content is transforming the landscape of creative processes. Immerse yourself in a domain where insights derived from data direct every facet of video production. These tools empower creators to customize content according to audience preferences, ensuring that each video is not only visually captivating but also strategically aligned. From scriptwriting informed by analytics to incorporating personalized visual elements, the utilization of data science takes video content to unprecedented levels. Delve into the crossroads of technology and creativity, where strategies driven by data redefine storytelling, captivating audiences in a personalized and meaningful manner.The Future of Data-Driven Creativity in Video ContentThe evolution of data-driven creativity in video content is set to transform our interaction with digital media. Through the incorporation of data science, creators gain valuable insights into viewer preferences, behavior, and trends. This collaboration enables a personalized and captivating viewing experience, heightening audience engagement. With the utilization of data-driven creativity, content producers can shape videos to suit the unique preferences of their target audience, resulting in more impactful storytelling and brand communication. As technology progresses, we anticipate a shift towards highly personalized content, driven by data insights, leading to innovative approaches in video production. This convergence of creativity and data science holds significant promise for the future development of video content within the digital landscape.ConclusionIn summary, the convergence of data-driven insights and creative components represents a transformative shift in the realm of video content creation. The fusion of Data Science and creativity provides content producers with the tools to precisely tailor videos to audience preferences, resulting in more impactful and engaging content. Leveraging the potential of data facilitates a deeper comprehension of viewer behavior, enabling targeted storytelling. Amidst the digital landscape, the symbiosis of data and creativity not only elevates video content but also fosters innovation and personalized experiences. Looking ahead, embracing Data-Driven Creativity becomes crucial for maintaining a leading edge in the continually evolving landscape of video content creation.

Join our private community in Discord

Keep up to date by participating in our global community of data scientists and AI enthusiasts. We discuss the latest developments in data science competitions, new techniques for solving complex challenges, AI and machine learning models, and much more!

Join our Discord