Q1. After training a classifier on imbalanced dataset, why is accuracy not a good metric? What metric would you use?
Accuracy can be high by always predicting majority class. Better metrics: precision, recall, F1-score, or ROC-AUC. Use classification_report:
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))Q2. For a regression problem, compute mean absolute error (MAE) and mean squared error (MSE) between true and predicted values.
from sklearn.metrics import mean_absolute_error, mean_squared_error
mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)Q3. Plot the confusion matrix for a binary classifier and compute sensitivity and specificity.
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
tn, fp, fn, tp = cm.ravel()
sensitivity = tp / (tp + fn)
specificity = tn / (tn + fp)Q4. Compute the ROC curve and AUC for a probabilistic classifier (e.g., LogisticRegression). Use predict_proba.
from sklearn.metrics import roc_curve, auc
y_prob = model.predict_proba(X_test)[:,1]
fpr, tpr, thresholds = roc_curve(y_test, y_prob)
roc_auc = auc(fpr, tpr)Q5. Compare multiple models using cross-validation scores. Use cross_val_score with scoring=''neg_mean_squared_error'' for regression.
from sklearn.model_selection import cross_val_score
scores = cross_val_score(model, X, y, cv=5, scoring=''neg_mean_squared_error'')
print(scores.mean(), scores.std())