All Episodes
Displaying 81 - 100 of 121 in total
Episode 80 — Regularization: Ridge, LASSO, Elastic Net as Control Knobs
This episode explains regularization as a stability and generalization control knob, because DataX scenarios frequently test whether you understand how Ridge, LASSO, a...
Episode 81 — Cross-Validation: k-Fold Logic and Common Misinterpretations
This episode teaches cross-validation as an estimation method for generalization performance, focusing on k-fold logic and the misinterpretations that DataX scenarios ...
Episode 82 — Hyperparameter Tuning: Grid vs Random vs Practical Constraints
This episode explains hyperparameter tuning as a constrained search problem, because DataX scenarios often test whether you can choose a tuning strategy that balances ...
Episode 83 — Class Imbalance: Why It Breaks Metrics and How to Fix Decisions
This episode addresses class imbalance as a decision and evaluation problem, because DataX scenarios frequently involve rare events where accuracy and naive thresholds...
Episode 84 — SMOTE and Resampling: When Synthetic Examples Help or Harm
This episode explains SMOTE and resampling as imbalance mitigation tools, focusing on when synthetic examples improve learning versus when they create false structure,...
Episode 85 — Generalization: In-Sample vs Out-of-Sample and Interpolation vs Extrapolation
This episode teaches generalization as the central promise and risk of machine learning, because DataX scenarios often ask whether a model will hold up beyond the data...
Episode 86 — Data Leakage: “Too Good to Be True” Results and How to Catch Them
This episode teaches data leakage as the most common reason models look perfect in evaluation and then collapse in production, which is why DataX scenarios repeatedly ...
Episode 87 — Drift Types: Data Drift vs Concept Drift and Expected Warning Signs
This episode distinguishes data drift from concept drift as two different reasons performance decays after deployment, because DataX scenarios often ask you to identif...
Episode 88 — Explainability: Global vs Local and Interpretable vs Post-Hoc
This episode teaches explainability as a spectrum of needs and methods, because DataX scenarios often include constraints like regulatory review, operational trust, or...
Episode 89 — Regression Families: When Linear Regression Is Appropriate
This episode reviews regression families with a focus on when linear regression is appropriate, because DataX scenarios often test whether you can defend linear regres...
Episode 90 — OLS Assumptions: What Violations Look Like in Real Problems
This episode teaches ordinary least squares assumptions as diagnostic signals rather than as a memorization list, because DataX scenarios often describe symptoms—unsta...
Episode 91 — Weighted Least Squares: Handling Non-Constant Variance in Regression
This episode explains weighted least squares as a targeted response to heteroskedasticity, because DataX scenarios may describe regression errors that grow or shrink a...
Episode 92 — Logistic Regression: Probabilities, Log-Odds, and Threshold Strategy
This episode teaches logistic regression as a probability model for classification, emphasizing how it represents outcomes through log-odds and why threshold strategy ...
Episode 93 — Logit vs Probit: Recognizing Differences Without Overcomplicating It
This episode explains logit versus probit as two closely related approaches for binary outcome modeling, focusing on what differences matter for DataX exam recognition...
Episode 94 — LDA vs QDA: Choosing Discriminant Methods by Data Shape
This episode teaches linear and quadratic discriminant analysis as probabilistic classification methods whose suitability depends on data shape assumptions, because Da...
Episode 95 — Naive Bayes: When Simple Probabilistic Models Shine
This episode explains Naive Bayes as a fast, practical probabilistic classifier that can perform surprisingly well when its conditional independence assumption is “wro...
Episode 96 — Association Rules: Support, Confidence, Lift, and Practical Meaning
This episode teaches association rules as pattern-mining outputs that describe co-occurrence relationships, because DataX scenarios may test whether you can interpret ...
Episode 97 — Decision Trees: Splits, Depth, Pruning, and Interpretability Tradeoffs
This episode explains decision trees as a rule-like model family, focusing on how splits create decision boundaries, how depth controls complexity, and how pruning sup...
Episode 98 — Random Forests: Bagging Intuition and Variance Reduction
This episode teaches random forests as an ensemble strategy for improving stability and generalization, because DataX scenarios often test whether you understand baggi...
Episode 99 — Boosting: Gradient Boosting and Why XGBoost Often Wins
This episode explains boosting as a sequential ensemble method that builds strong predictors by combining many weak learners, emphasizing gradient boosting intuition a...