ML Zoomcamp Classification Module Recap: Why Mutual Information Beats Correlation Analysis

The first step in a classification task is often not choosing a model, but figuring out which features are actually useful. Module 3 of ML Zoomcamp 2026 revolves around this question, covering logistic regression training, one-hot encoding for categorical variables, mutual information scoring, feature elimination, and the interpretability of classification models. One student shared their study notes and two key findings on Dev.to, offering useful insights for anyone working on tabular data classification.

The Basic Toolkit for Classification Tasks

The tech stack for this module is minimal, but every step has a clear purpose.

Logistic regression serves as the starting point for classification models, producing probability outputs and defining decision boundaries. Categorical variables can't be thrown directly into a model, so one-hot encoding expands each category into independent binary features, allowing the model to quantify how different categories affect the target.

Mutual information scoring measures the dependency between a single feature and the target. It's equally sensitive to non-linear relationships, unlike Pearson correlation which only captures linear ones. Feature elimination works by removing features one at a time and observing performance changes, reverse-validating which features are truly contributing predictive power.

The final step is model interpretation — understanding what a trained classification model has actually learned, not just whether its predictions are accurate.

Mutual Information originates from information theory and measures the statistical dependency between two random variables — how much uncertainty about one variable is reduced by knowing the other. Unlike Pearson correlation which only measures linear relationships, mutual information can capture any form of dependency, including non-linear and non-monotonic relationships, and applies equally to combinations of categorical and numerical variables. Its values range from 0 (complete independence) to the smaller of the variables' entropies (complete dependence), making it a common scoring criterion for filter-based feature selection. In practice, sklearn.feature_selection.mutual_info_classif and mutual_info_regression estimate probability densities based on k-nearest neighbors. Results can fluctuate with small sample sizes, so it's recommended to fix the random seed and average across multiple runs.

One-Hot Encoding expands a discrete variable with k categories into k binary columns, where each sample takes 1 in the corresponding category column and 0 elsewhere. This avoids the trap of interpreting category codes as numerical magnitudes and allows linear models to learn independent weights for each category. Two things to note: first, it produces high-dimensional sparse matrices, so target encoding or embeddings should be used when category cardinality is large; second, there's the "dummy variable trap" — if all k columns are retained while fitting an intercept, features become perfectly collinear. While logistic regression can mitigate this through regularization, it's typically still advisable to use drop='first' to remove one column. The choice of encoding scheme can sometimes slightly affect feature importance and accuracy, which is exactly why the article re-verified a 0.002 difference using a second scheme.

The Top-Ranked Feature by Correlation May Not Be Truly Useful

The student's most important takeaway was recognizing that correlation and mutual information answer two different questions.

In the dataset used, the pair with the highest value in the correlation matrix was interaction_count and lead_score. If feature importance were ranked solely by correlation, interaction_count would seem to be a priority to retain. But when switching to mutual information scoring, the feature with the highest mutual information with the target variable was lead_source — a categorical feature.

The problem is that the correlation matrix essentially computes linear relationships between numerical columns, and categorical features simply cannot enter this matrix. In other words, based on correlation ranking alone, lead_source's importance would be completely overlooked.

If a feature set were built based on correlation results, the resulting model would miss a key categorical feature while overestimating the importance of interaction_count. This case demonstrates that the choice of tools during feature selection directly determines what the model can and cannot see.

A 0.002 Accuracy Difference Is Worth Re-verifying

The second finding relates to experimental methodology.

In a feature elimination exercise, removing number_of_courses_viewed caused accuracy to drop by only 0.002, while removing interaction_count caused a drop of 0.044. The difference was more than twentyfold, and the former appeared to be noise — likely just random fluctuation.

Rather than dismissing it as noise, he re-ran the entire comparison using an alternative one-hot encoding scheme to confirm whether the result was due to library implementation details. The conclusion was definitive: the tiny difference was real, not random error.

What was right about this approach: small differences are easily dismissed as noise, but before confirming it's actually noise, the cost of re-verification is low while the cost of misjudgment can be high. If the 0.002 difference actually represents a feature's value that only manifests under a specific encoding scheme, ignoring it means losing room for improvement.

In practice, a few things to watch when re-running experiments: fix the random seed, ensure all other conditions are consistent across the two encoding schemes, and compare variance across multiple runs rather than a single result. Only then do conclusions hold up to scrutiny.

Why Interpretability of Classification Models Matters

The module specifically covers "interpreting what a classification model has learned," which is more important in real projects than many people realize.

When a classification model is deployed in a business context — such as lead scoring or risk identification — users need to know why the model made a particular judgment. Mutual information scoring and feature elimination provide exactly this kind of interpretability: they tell you which features are driving predictions and which ones matter little when removed.

This understanding also helps with troubleshooting. If a model performs abnormally after deployment, knowing the ranking of feature importance allows you to quickly pinpoint which input stage has a problem.

The student's next step is Module 4, which covers evaluation metrics for classification. Moving from feature selection to model evaluation is logically coherent: first ensure the features fed to the model are correct, then ensure the standards used to measure model performance are reasonable.

For readers currently learning machine learning classification, the two practical lessons from this module are worth remembering: use mutual information to supplement correlation analysis, and don't let categorical features be excluded by matrix computations; when encountering unexpected small differences, take the time to verify them rather than immediately dismissing them as noise.

Feature Elimination belongs to wrapper-based feature selection, with the typical implementation being Recursive Feature Elimination (RFE): repeatedly train the model and remove the weakest features by weight or importance until the target number is reached. Its advantage is using model performance directly as the criterion, capturing interaction effects between features; the cost is that computational overhead grows with the number of features, and performance fluctuations on small datasets are easily misread as real signals. Therefore, when evaluating whether a feature is worth retaining, focus on the mean and variance of accuracy differences across multiple cross-validations, not just the result of a single split. This aligns with the article's approach of "re-verifying a 0.002 difference."

One-line Summary

ML Zoomcamp classification notes: mutual information reveals categorical features missed by correlation, and tiny accuracy differences need re-verification.

Paragraph Summary

A student recorded two key findings in ML Zoomcamp 2026 Module 3. First, correlation and mutual information answer different questions: the correlation matrix only computes linear relationships between numerical columns, so categorical features like lead_source are completely excluded; after switching to mutual information scoring, it turns out to be the feature most strongly dependent on the target, and ranking by correlation alone would produce a model that misses key variables. Second, removing a feature in feature elimination caused only a 0.002 accuracy drop, seemingly noise, but re-running with a second one-hot encoding scheme confirmed the difference was real, showing that small differences are worth low-cost re-verification before being dismissed as noise. The article also emphasizes the practical significance of classification model interpretability and notes that feature selection and model evaluation form a coherent workflow.