P-3: Practice Set | Machine Learning.

Описание: In this lecture, we solve four questions from a practice set on Machine Learning.

Question Included:
6. A logistic regression model is trained to predict whether a tumor is malignant (1) or benign (0) based on cell size and age:
𝑧=−5.2+0.15×CellSize+0.03×Age
Compute the probability of malignancy for a 45-year-old patient with CellSize = 28. Interpret the result.

7. A dataset of apartments shows the following relationship: Area (sq.ft) 𝑋: [600, 800, 1000, 1200, 1500] Rent (₹ per month) 𝑌: [10,000, 13,000, 16,000, 19,000, 24,000] Compute the slope and intercept for the simple linear regression model and predict the rent for an apartment with 1100 sq.ft area.

8. A real-estate analyst predicts house prices using features such as area, number of rooms, location rating, and house age. Two models are trained:
Model A: Multiple Linear Regression
Model B: Lasso Regression
Both fit the training data well, but Model B’s predictions on test data are more stable and rely on fewer features. (a) Compare the two models in terms of feature selection and generalization ability. (b) Explain how Lasso’s L1 regularization helps prevent overfitting and improve interpretability.

9. A retail analytics team applies Ridge Regression to forecast monthly sales using features such as advertising budget, price discounts, seasonal index, and competitor activity. a) Demonstrate how adjusting λ reduces the effect of multicollinearity among promotional features (e.g., advertising vs discount). b) Discuss differences between Ridge and Lasso in handling redundant predictors and explain which model would better retain correlated sales drivers. c) Recommend an evidence-based method to tune λ for maximizing predictive stability during future campaigns.

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