Machine Learning Interview Questions in Hyderabad

Show answer

Use ColumnTransformer inside a Pipeline so preprocessing is fit only on training folds. from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder num = Pipeline([('imp', SimpleImputer(strategy='median')), ('sc', StandardScaler())]) pre = ColumnTransformer([ ('num', num, num_cols), ('cat', OneHotEncoder(handle_unknown='ignore'), cat_cols) ]) model = Pipeline([('pre', pre), ('clf', LogisticRegression(max_iter=1000))])

How do you handle class imbalance?

Show answer

Start with the right metrics, such as PR-AUC, recall at fixed precision or F1, not accuracy. Then use class_weight='balanced', resampling like random undersampling or SMOTE applied only on training folds, and threshold tuning based on business cost. Boosting models with scale_pos_weight also help. Stratified splits keep class ratios consistent across folds.

Use HistGradientBoosting with native categorical support in scikit-learn.

Decision Trees and EnsemblesMedium

Show answer

from sklearn.ensemble import HistGradientBoostingClassifier clf = HistGradientBoostingClassifier( categorical_features='from_dtype', max_iter=500, learning_rate=0.05, early_stopping=True, random_state=42) X_train[cat_cols] = X_train[cat_cols].astype('category') clf.fit(X_train, y_train) It handles missing values natively and is much faster than GradientBoostingClassifier on large data.

How do you calculate VIF for features in Python?

Show answer

Use statsmodels on the numeric feature matrix with a constant added. import pandas as pd from statsmodels.stats.outliers_influence import variance_inflation_factor from statsmodels.tools import add_constant Xc = add_constant(X_num) vif = pd.Series([variance_inflation_factor(Xc.values, i) for i in range(Xc.shape[1])], index=Xc.columns) print(vif.drop('const')) Values above 5 to 10 indicate strong multicollinearity.

How do you choose a classification threshold that maximises F1?

Show answer

from sklearn.metrics import precision_recall_curve import numpy as np proba = clf.predict_proba(X_val)[:, 1] p, r, t = precision_recall_curve(y_val, proba) f1 = 2 * p * r / (p + r + 1e-9) best = t[np.argmax(f1[:-1])] print(best) Choose it on validation data, not the test set. scikit-learn 1.5+ also offers TunedThresholdClassifierCV.

Explain the bias in AUC when classes are extremely imbalanced, with a numeric intuition.

Model Evaluation and TuningHard

Show answer

With 100 fraud and 100,000 genuine cases, a false positive rate of 1 percent looks tiny on the ROC curve, but it means 1,000 false alerts versus at most 100 true ones, so precision is below 10 percent. ROC-AUC can be 0.97 while the model is impractical. Precision-recall curves expose this because precision directly depends on the count of false positives.

Visualise a small decision tree and its rules.

Decision Trees and EnsemblesEasy

Show answer

from sklearn.tree import DecisionTreeClassifier, export_text, plot_tree import matplotlib.pyplot as plt dt = DecisionTreeClassifier(max_depth=3, random_state=0).fit(X_train, y_train) print(export_text(dt, feature_names=list(X_train.columns))) plot_tree(dt, feature_names=X_train.columns, filled=True) plt.show() Shallow trees are a good way to explain rules to business teams.

What is the log loss metric and why is it useful?

Model Evaluation and TuningMedium

Show answer

Log loss, or cross-entropy, is the negative average log of the probability assigned to the true class. It heavily penalises confident wrong predictions, such as predicting 0.99 for an event that does not happen. It evaluates probability quality, not just ranking, so it suits problems where probabilities drive decisions. Lower is better, and a constant base-rate prediction is the baseline to beat.

Validation AUC is 0.99 on a loan default model. What do you suspect?

Show answer

Such a high score on a hard problem usually signals leakage. I would check feature importances for a suspiciously dominant feature, look for fields recorded after the loan outcome such as collection status, verify preprocessing was fit only on training data, and check for duplicate customers across train and test. Then re-run with a time-based split to confirm a realistic score.

Tell me about a time you balanced model accuracy against a business constraint.

Show answer

Situation: our lead-scoring model had the best AUC with a large ensemble, but the CRM needed scores within 50 ms. Task: meet latency without losing much quality. Action: I compared a smaller LightGBM model with fewer features and measured both AUC and latency. Result: we lost 0.01 AUC but met the 50 ms limit, and sales teams adopted it because scores appeared instantly.

Tell me about a time your analysis was not used by stakeholders. What did you learn?

Show answer

Situation: I built eight customer segments, but marketing did not use them. Task: understand why. Action: I met the team and learned the segments were hard to act on and lacked clear names. I reduced them to five, added a one-page profile and suggested campaign ideas for each. Result: two segments were used in the next campaign. I learned to involve users from the start.

What is boosting and how is it different from bagging?

Decision Trees and EnsemblesMedium

Show answer

Boosting trains models sequentially, each one focusing on the errors of the combined previous models, which mainly reduces bias. Bagging trains models independently in parallel and averages them, which mainly reduces variance. Boosting uses shallow trees and a learning rate, and can overfit if run too long, so early stopping is important. Examples are AdaBoost, Gradient Boosting, XGBoost, LightGBM and CatBoost.

What is the difference between offline evaluation and online A/B testing of a model?

Model Evaluation and TuningMedium

Show answer

Offline evaluation measures metrics like AUC on historical holdout data; it is fast and cheap but cannot capture user behaviour changes or feedback loops. Online A/B testing exposes a share of real traffic to the new model and measures business outcomes like conversion or loss rate. Offline gains do not always translate online, so important models are validated with A/B tests or shadow deployments.

How do you create polynomial features with regularisation in a pipeline?

Regression ModelsMedium

Show answer

from sklearn.preprocessing import PolynomialFeatures, StandardScaler from sklearn.linear_model import Ridge from sklearn.pipeline import make_pipeline model = make_pipeline( PolynomialFeatures(degree=2, include_bias=False), StandardScaler(), Ridge(alpha=1.0) ) model.fit(X_train, y_train) Tune degree and alpha together with GridSearchCV to avoid overfitting.

You must build a spam classifier quickly with limited compute. What do you choose?

Classification ModelsEasy

Show answer

I would use TF-IDF features with a Multinomial Naive Bayes or linear model like logistic regression or LinearSVC. They train in seconds, handle high-dimensional sparse text well and give strong baselines. I would evaluate precision carefully because blocking genuine mail is costly. Only if this is not good enough would I try fine-tuning a small transformer model.

Your dataset has 800 correlated features and the model trains slowly. What would you do?

Show answer

I would remove constant and near-duplicate features, drop one from each highly correlated pair, then apply PCA inside the pipeline and keep components explaining about 95 percent of variance. Alternatively, use L1-regularised models or tree importances for supervised selection. I would compare speed and cross-validated scores; often 50 to 100 features give nearly the same accuracy.

Leadership wants a model in one week, but data quality is poor. How do you handle it?

ML FundamentalsMedium

Show answer

I would agree on a narrow first version: a simple, explainable baseline built on the cleanest available fields, with clear notes on data gaps and expected accuracy. In parallel I would log specific data issues and their impact. Delivering a working baseline with an honest limitations section builds trust and gives a reference point for improvement in later iterations.

What is association rule mining? Explain support, confidence and lift.

Show answer

Association rule mining finds items that occur together, such as in market basket analysis. Support is how often an itemset appears in all transactions. Confidence of A to B is the share of transactions with A that also contain B. Lift is confidence divided by B's support; lift above 1 means A and B occur together more than by chance. Apriori and FP-Growth are common algorithms.

What is heteroscedasticity and why does it matter?

Regression ModelsMedium

Show answer

Heteroscedasticity means the variance of residuals changes with the level of the prediction, such as errors growing for expensive houses. Predictions remain unbiased, but standard errors and p-values become unreliable. Detect it with a residuals versus fitted plot or the Breusch-Pagan test. Fixes include log-transforming the target, weighted least squares, or robust standard errors.

Your training loss is not decreasing at all. What could be wrong?

ML FundamentalsHard

Show answer

Common causes are a learning rate that is too high or too low, unscaled features, a bug in labels or feature alignment, wrong loss for the task, or data that has been shuffled away from its labels. I would try overfitting a tiny subset first; if the model cannot even fit 50 rows, there is a bug. Then check scaling, learning rate and gradient values.

The fraud team can only review 500 alerts a day. How do you set the threshold?

Show answer

Instead of 0.5, rank transactions by predicted probability and pick the threshold that yields about 500 alerts per day on recent data. Then report precision and recall at that cut-off, or precision at k. I would monitor daily alert volume because drift can change it, and recompute the threshold periodically. If cost data exists, I would choose the threshold that maximises expected savings.

Detect anomalies with Isolation Forest.

Show answer

from sklearn.ensemble import IsolationForest iso = IsolationForest(n_estimators=300, contamination=0.01, random_state=42) iso.fit(X_train) scores = -iso.score_samples(X_new) flags = iso.predict(X_new) == -1 Higher scores mean more anomalous. Tune contamination to match how many alerts reviewers can handle.

A business stakeholder asks you to use deep learning for a 5,000-row tabular dataset. How do you respond?

ML FundamentalsMedium

Show answer

I would explain that for small tabular data, gradient-boosted trees or regularised linear models usually match or beat neural networks, train faster and are easier to explain. I would propose building a simple baseline, then a boosting model, and only test a neural network if there is a clear reason. I would show the comparison on cross-validated metrics so the decision is data-driven.

Your dataset has a city column with 2,000 unique values. How will you encode it?

Show answer

One-hot would create 2,000 sparse columns, so I would first group rare cities below a frequency threshold into 'Other'. Then options are frequency encoding, out-of-fold target encoding, or mapping cities to useful attributes such as tier, state or population. For boosting models like CatBoost or LightGBM, native categorical handling also works well. I would compare options with cross-validation.

Your house price model's residuals grow larger for expensive houses. What do you do?

Regression ModelsMedium

Show answer

That is heteroscedasticity, typical for price data that is right-skewed. I would model log(price) instead of price, which turns absolute errors into roughly percentage errors and stabilises variance. Then I would convert predictions back with exp and check residual plots again. Evaluating with RMSLE or MAPE also fits better when relative error matters more than absolute error.

What is data drift and concept drift?

MLOps and DeploymentMedium

Show answer

Data drift, or covariate shift, means the distribution of input features changes, such as average order value rising after a pricing change. Concept drift means the relationship between features and target changes, such as fraud patterns evolving. Data drift can be detected without labels using PSI or KS tests; concept drift usually needs labels and shows up as falling performance. Both trigger investigation and retraining.

Plot a learning curve in scikit-learn.

Model Evaluation and TuningMedium

Show answer

from sklearn.model_selection import LearningCurveDisplay import numpy as np LearningCurveDisplay.from_estimator(model, X, y, cv=5, train_sizes=np.linspace(0.1, 1.0, 6), scoring='roc_auc', n_jobs=-1) plt.show() A persistent gap between curves suggests more data or regularisation; two low curves suggest a richer model or better features.

How do you create lag and rolling features for a sales forecasting model in pandas?

Regression ModelsHard

Show answer

Sort by item and date, then shift so features use only past values. df = df.sort_values(['item', 'date']) g = df.groupby('item')['sales'] df['lag_7'] = g.shift(7) df['roll_mean_28'] = g.transform(lambda s: s.shift(1).rolling(28).mean()) The shift before rolling prevents the current day's sales from leaking into its own feature.

Log a training run to MLflow.

MLOps and DeploymentMedium

Show answer

import mlflow, mlflow.sklearn mlflow.set_experiment('churn') with mlflow.start_run(): pipe.fit(X_train, y_train) auc = roc_auc_score(y_val, pipe.predict_proba(X_val)[:, 1]) mlflow.log_params({'C': 1.0, 'model': 'logreg'}) mlflow.log_metric('val_auc', auc) mlflow.sklearn.log_model(pipe, 'model', registered_model_name='churn-model')

How do you interpret a coefficient in a multiple linear regression?

Regression ModelsEasy

Show answer

A coefficient is the expected change in the target for a one-unit increase in that feature, holding other features constant. For example, a coefficient of 2,500 on 'area in square metres' means each extra square metre adds about 2,500 to price. Interpretation breaks down with multicollinearity or when features are scaled, so report units clearly and check stability.

Run stratified 5-fold cross-validation with multiple metrics.

Model Evaluation and TuningEasy

Show answer

from sklearn.model_selection import StratifiedKFold, cross_validate cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=42) res = cross_validate(pipe, X, y, cv=cv, scoring=['roc_auc', 'average_precision', 'f1'], n_jobs=-1) for k in ['test_roc_auc', 'test_average_precision', 'test_f1']: print(k, res[k].mean().round(3), res[k].std().round(3))

Marketing wants customer segments for campaigns. How do you approach it?

Show answer

I would agree on the purpose first, such as retention or upsell. Then build RFM features (recency, frequency, monetary) plus category preferences, log-transform skewed values and scale them. I would try k-means for k between 3 and 8, use silhouette and business review to pick k, profile each segment and give them clear names. Finally, test segment-based campaigns against a control group.

How do one-vs-rest and multinomial approaches differ for multiclass classification?