Understanding Cross Validation | How Cross Validation Helps Us Avoid The Pitfalls Of Overfitting

Tony Yiu
Jun 04, 2020

Contents Outline

Understanding Cross Validation | How Cross Validation Helps Us Avoid The Pitfalls Of Overfitting

Jun 04, 2020 7 minutes read

I remember the first quantitative model that I ever built. It was a model that tactically shifted money between U.S. stocks and Emerging Market stocks based on various market and economic factors that I had researched.

I spent a ton of time engineering (over-engineering actually) my features and running backtests until I was sure that I had a world beater. When my model was finally put into production, it spent a year doing basically nothing. Ultimately, I think it produced a slightly negative cumulative return.

I count myself lucky. Thinking back, I realize that my model was massively overfit and I’m fortunate that the bets it recommended did not blow up and cost my firm significant amounts of money.

Recognizing When A Model Is Overfit

Today we will discuss cross validation, a technique that helps us estimate the out of sample performance of our model and prevent overfitting. But first, let’s talk a bit about what overfitting is and why it occurs.

Overfitting is when we train our model so much to the existing data that it loses the ability to generalize. Models that generalize well are ones that can adapt reasonably successfully to new data, especially ones that are unlike any of the observations the model has seen up until now. So if an overfit model is unable to generalize, then it’s highly likely to perform erratically (and probably badly) when it gets put into production and truly goes out of sample. The most common causes of overfitting are:
  • Spurious correlations: if we look hard enough, we will find strong correlations. For example, we might find that the price of Bitcoin is highly correlated to the price of pizza in Zimbabwe. But that’s most likely due to chance and randomness, not anything real, and we would be foolish to bet money on such a correlation. If we fit our model with a bunch of factors that are spuriously correlated to the thing that we are trying to predict, it will not generalize well.
  • Overuse of the test set: This one is really hard to completely avoid. If a model doesn’t work well on our test set (the test set is the portion of our data that we hold out so that we can assess how the model generalizes on new data), then we will tweak it until we find a configuration that works well on both the training set and test set. The implication of doing so is that the test set is no longer an unbiased estimate of our model’s out of sample performance — after all, once we start making modeling decisions using the hold out set (a.k.a. the test set), then can we really consider it still to be held out?
  • A biased training set: Our training data will rarely ever be truly representative of the population that we are trying to model. So we should be aware that we are virtually guaranteed to run into data that our model finds completely unfamiliar at some point. And while we should do our best to match the characteristics of our sample to that of the overall population, we should also know the areas where the sample falls short. Because it is data from these areas that represent the greatest risk to our model. If our sample is representative of only a small portion of our population, then our model will perform poorly over time.



How Cross Validation Helps


Cross validation is a technique that allows us to produce test set like scoring metrics using the training set. That is, it allows us to simulate the effects of “going out of sample” using just our training data, so we can get a sense of how well our model generalizes.

Without cross validation, the traditional model training process looks like this:


Traditional train-test split

We train on the blue part until we feel like our model is ready to face the wild. Then we score it on the test set (the gold part). The drawback of the traditional way is that we only get one shot at things. The moment we test our model on the test set, we’ve compromised our test data. And if our test set results were terrible, what then? Would we really be alright with throwing away all those hours of work or would we just start optimizing our results for the test set?

If only there were a way to simulate how our model might perform on the test set without actually using the test set. There is! And it’s called cross validation.

Cross Validation

We will focus on a specific type of cross validation called K-folds (so when I merely say cross validation, I mean K-folds cross validation). K-folds cross validation splits our training data into K folds (folds = subsections). We then train and test our model K times so that each and every fold gets a chance to be the pseudo test set, which we call the validation set. Let’s use some visuals to get a better understanding of what’s going on:

3-Folds Cross Validation

Say we are developing a predictive linear regression model and are using R² as our primary scoring metric. We have some data that we have split into a training set and a test set. Our primary concern is the accuracy of the out of sample predictions of our model (how well it generalizes). Thus, we‘ve decided to not look at the test set until the very end (so we can give our model an intellectually honest grade).

But as we tweak and refine our model, we still want to get a sense of how the changes we are making might affect its out of sample performance. So we cross validate:
  • We decide to run 3-folds cross validation, meaning that we split our training data into 3 folds of equal size.
  • In Run 1 of our cross validation, Fold 1 is held out (the pink rectangle labeled Hold Out 1). So we train on the training data (blue rectangle) that is not in Fold 1, and then validate on Fold 1. This means that we fit our model using the non-Fold 1 training data, and then calculate and record how well we predicted the observations of the dependent variable in Fold 1. Crucially, in Run 1 we did not use any of the data in Fold 1 during the training of our model, so the R² calculated using Fold 1 is sort of like an out of sample R².
  • In Run 2, Fold 2 is held out. Now Fold 1, which was previously our validation set, has become part of our training set. We fit our model using the data in Fold 1 and Fold 3, and score it using Fold 2 (via R²).
  • After Run 3 concludes, we now have three R² values (since each fold gets a turn to be held out). The average of the three R²s gives us a decent estimate of the out of sample R² of our model.

The most important thing to remember about how cross validation works is that in each run, the scoring metrics it reports are calculated on just the fold that was held out.

Conclusion


A few tips when employing cross validation:
  1. It’s important to keep in mind that the cross validation score (such as R²) of our model is at best an optimistic estimate of its performance on the test set. Just like how the performance on the test set is at best an optimistic estimate of the model’s true ability to generalize.
  2. If a change we made to the model increases the training score (which is estimated in sample) but decreases the cross validation score, then that’s a good sign that we are overfitting the model.
  3. Having too few folds hamstrings the model — this is because too much of the training data gets held out in each cross validation run. For example with 2 folds, half the training data is held out, which means that the model is only fit on the remaining half (resulting in scoring metrics that are lower than they should be). An under-fit model generalizes just as badly as an overfit one.
  4. It’s good practice to shuffle the data before we train test split in case the data was sorted. If it were sorted in some way and we neglected to shuffle it, then our train test split would provide biased data sets, where neither one would be a good representative of the actual population.
  5. Once we’re done refining the model via cross validation, we should refit the model on the entire training set before testing it on the test set.


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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. 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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.

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