Machine Learning Interview Questions in Pune

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Likely causes are unscaled features, where one large-range feature dominates distance; heavy skew or outliers pulling centroids; or data that is not naturally spherical. I would log-transform and scale features, remove or cap extreme outliers, and try different k values. If shapes are irregular, DBSCAN, HDBSCAN or a Gaussian Mixture Model may fit better.

Apply PCA and check explained variance.

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from sklearn.decomposition import PCA import numpy as np pca = PCA().fit(Xs) cum = np.cumsum(pca.explained_variance_ratio_) n = np.argmax(cum >= 0.95) + 1 print(n, 'components explain 95 percent') X_red = PCA(n_components=0.95).fit_transform(Xs) Passing a float to n_components keeps enough components to reach that variance.

How do you approach a time series forecasting problem differently from normal regression?

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Time series data is ordered and autocorrelated, so random splits leak future information. I use time-based splits or TimeSeriesSplit, create lag and rolling-window features from past values only, and handle trend and seasonality. Baselines like last value or seasonal naive come first. Models range from ARIMA and Prophet to gradient boosting on lag features.

What is the difference between batch, stochastic and mini-batch gradient descent?

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Batch gradient descent uses the full dataset for each update, giving stable but slow and memory-heavy steps. Stochastic gradient descent updates after every single example, which is fast and noisy and can escape shallow minima. Mini-batch uses small groups such as 32 to 512 rows, balancing stability with speed and allowing vectorised computation. Mini-batch is the default in practice.

How do you save and load a scikit-learn model?

MLOps and DeploymentEasy

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Save the whole fitted pipeline so preprocessing travels with the model. import joblib joblib.dump(pipe, 'model_v1.joblib') pipe = joblib.load('model_v1.joblib') preds = pipe.predict(new_df) Load with the same scikit-learn version used for training, and never load pickle or joblib files from untrusted sources because they can execute code. ONNX or skops are safer portable options.

What does it mean to deploy a machine learning model?

MLOps and DeploymentEasy

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Deployment makes a trained model available to produce predictions for real users or systems. It can be batch, scoring data on a schedule and writing results to a table, or real-time, serving predictions through a REST API, or streaming, scoring events as they arrive. It includes packaging the model with its preprocessing, setting up infrastructure, logging, monitoring and a retraining plan.

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

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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.

How do you check regression residuals visually in Python?

Regression ModelsEasy

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import matplotlib.pyplot as plt res = y_test - pred fig, ax = plt.subplots(1, 2, figsize=(10, 4)) ax[0].scatter(pred, res, s=8); ax[0].axhline(0, color='red') ax[0].set_xlabel('Predicted'); ax[0].set_ylabel('Residual') ax[1].hist(res, bins=40) plt.show() A funnel shape suggests heteroscedasticity; a curve suggests a missing non-linear term.

Find the most similar items using cosine similarity.

Data Preprocessing and Feature EngineeringHard

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from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.neighbors import NearestNeighbors X = TfidfVectorizer(min_df=2).fit_transform(df['title']) nn = NearestNeighbors(n_neighbors=6, metric='cosine').fit(X) dist, idx = nn.kneighbors(X[0]) print(df['title'].iloc[idx[0][1:]].tolist()) The first neighbour is the item itself, so skip it.

Why does L1 regularisation produce sparse models while L2 does not?

Regression ModelsHard

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The L1 constraint region is a diamond with corners on the axes, so the optimal point often lands on a corner where some coefficients are exactly zero. The L2 region is a circle with no corners, so coefficients shrink smoothly but stay non-zero. In gradient terms, L1 applies a constant push toward zero regardless of size, while L2's push becomes tiny as weights get small.

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

Classification ModelsMedium

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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.

Explain bagging and how a random forest uses it.

Decision Trees and EnsemblesEasy

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Bagging trains many models on bootstrap samples, random samples with replacement, and averages their predictions to reduce variance. A random forest adds feature randomness: each split considers only a random subset of features, controlled by max_features. This de-correlates the trees, so averaging helps more. Out-of-bag samples, about 37 percent left out per tree, give a free validation estimate.

What is training-serving skew and how do you prevent it?

MLOps and DeploymentHard

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Training-serving skew is when features at prediction time are computed differently from features used in training, causing silent accuracy loss. Causes include separate code paths, different data sources, or time windows that use future data in training. Prevent it by sharing one feature-computation code path or a feature store, logging served features and comparing their distributions with training data.

Grid search with 5 hyperparameters is taking two days. How do you speed it up?

Model Evaluation and TuningMedium

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Switch to random search or Optuna with pruning, which find good settings with far fewer trials. Narrow ranges using a quick coarse search, tune the most important parameters first, use early stopping for boosting, run trials in parallel with n_jobs, and tune on a stratified sample of the data before a final refit on the full set. HalvingRandomSearchCV is another quick option.

What is target encoding and how do you avoid leakage with it?

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Target encoding replaces each category with the mean target value for that category, which works well for high-cardinality features. It leaks if computed on the full data because each row sees its own label. Avoid this by computing encodings out-of-fold within cross-validation and adding smoothing toward the global mean for rare categories. scikit-learn 1.3+ has TargetEncoder with built-in cross-fitting.

What is a holdout set and why should it be touched only once?

Model Evaluation and TuningEasy

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A holdout or test set is kept aside for the final evaluation after all model and hyperparameter choices are done. If you check it repeatedly and adjust the model, you are indirectly tuning on it, so its score becomes optimistic. Use cross-validation or a separate validation set for decisions, and report the holdout score once as the realistic estimate.

What are shadow deployment and canary release for models?

MLOps and DeploymentMedium

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In shadow deployment the new model receives a copy of live traffic and its predictions are logged but not used, so you can compare it with the current model safely. In a canary release a small share of real traffic, such as 5 percent, is served by the new model, and the share grows if metrics stay healthy. Both reduce the risk of a bad model affecting all users.

How do you implement simple gradient descent for linear regression in NumPy?

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Compute predictions, the gradient of MSE, and update weights in a loop. import numpy as np w = np.zeros(X.shape[1]); b = 0.0; lr = 0.01 for _ in range(1000): err = X @ w + b - y w -= lr * (2 / len(y)) * (X.T @ err) b -= lr * (2 / len(y)) * err.sum() Scale features first or the loop converges slowly.

How do you handle disagreement with a senior colleague about model choice?

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I keep it about evidence. In one project a senior preferred a complex model while I proposed regularised regression. I suggested we compare both on the same cross-validation folds and also measure inference time and explainability. The complex model was only 1 percent better, so we chose the simpler one. I made sure to credit his idea of the extra features, which we kept.

Your random forest model file is 2 GB and too slow for real-time scoring. What can you do?

Decision Trees and EnsemblesMedium

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Reduce size by limiting max_depth and min_samples_leaf, using fewer trees since gains flatten after a few hundred, and dropping weak features. Alternatively switch to a gradient boosting model like LightGBM, which usually gives similar accuracy with far smaller, faster models. Converting to ONNX or using a compiled predictor can also speed up inference. Measure accuracy and latency after each change.

How do you calculate VIF for features in Python?

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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.

What is the curse of dimensionality?

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As the number of features grows, data becomes sparse in the feature space, distances between points become similar, and the amount of data needed to generalise grows rapidly. Distance-based methods like k-NN and k-means suffer most. Remedies include feature selection, dimensionality reduction such as PCA, regularisation and collecting more data. Text and genomics data often face this problem.

Tell me about a time you took a model from notebook to production.

MLOps and DeploymentMedium

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Situation: our demand model ran only in a notebook on my laptop. Task: make it run daily for 300 stores. Action: I refactored it into modules with a scikit-learn pipeline, added tests and input checks, containerised it with Docker and scheduled it with Airflow writing forecasts to the warehouse. Result: forecasts became available by 6 a.m. daily without manual work, and failures triggered alerts instead of silent gaps.

What is boosting and how is it different from bagging?

Decision Trees and EnsemblesMedium

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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.

40 percent of values in an important income column are missing. What do you do?

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I would study the missingness pattern first, for example whether self-employed applicants skip it. I would add a binary was_missing feature, impute with the median within relevant groups or use IterativeImputer, and compare against models that handle missing values natively, like HistGradientBoosting or XGBoost. I would also raise the issue with the data owner so collection can improve.

When should you use TimeSeriesSplit or GroupKFold instead of normal k-fold?

Model Evaluation and TuningMedium

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Use TimeSeriesSplit when data is ordered in time, so each validation fold comes after its training data and no future information leaks back. Use GroupKFold when multiple rows belong to the same entity, such as several visits per patient, so the same entity never appears in both train and validation. Normal k-fold in these cases gives over-optimistic scores.

What are overfitting and underfitting, and how do you detect them?

ML FundamentalsEasy

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Overfitting is when a model memorises training noise and performs much worse on unseen data. Underfitting is when the model is too simple to capture the pattern, so both training and validation scores are poor. Detect them by comparing training and validation metrics or plotting learning curves. A large gap means overfitting; two low, close scores mean underfitting.

Describe a project where you improved a model's performance significantly.

Decision Trees and EnsemblesMedium

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Situation: a product-return prediction model had 0.68 AUC with logistic regression. Task: improve it for a warehouse pilot. Action: I engineered features like past return rate per customer and category, moved to LightGBM with early stopping, and tuned it with Optuna on time-based folds. Result: AUC rose to 0.81, and the pilot reduced avoidable returns in two categories. I documented each step's gain.

Why do skewed features often need a log or power transform?

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Heavily right-skewed features like income or transaction amount have long tails that can dominate linear models and distance metrics. A log1p, Box-Cox or Yeo-Johnson transform compresses large values and makes the distribution closer to normal, which often improves linear and distance-based models. Yeo-Johnson works with zero and negative values. Tree models usually do not need these transforms.

What is the kernel trick?

Classification ModelsHard

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The kernel trick computes dot products of data in a high-dimensional feature space without explicitly transforming the data, using a kernel function such as RBF, exp(-gamma times squared distance). This lets linear algorithms like SVM learn non-linear boundaries efficiently. In RBF SVMs, larger gamma makes each point's influence more local, which can lead to overfitting.

A demand forecast is consistently under-predicting during festivals. How would you fix it?

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The model lacks festival information. I would add features for festival dates such as Diwali or regional holidays, days-to-festival, and past festival uplift for the same item. If the business cost of stock-outs is higher than over-stock, I would use a quantile loss for a higher percentile. Then validate specifically on past festival periods, not just the average error.

Write a simple Dockerfile for an ML API.

MLOps and DeploymentMedium

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FROM python:3.12-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY app.py model_v1.joblib ./ EXPOSE 8000 CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"] The exec-form CMD avoids a shell wrapper. Pin versions in requirements.txt so training and serving match.

How do you set up a basic train-test split with scikit-learn?

ML FundamentalsEasy

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Use train_test_split with a fixed random_state for reproducibility and stratify for classification so class ratios match. from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) For time series, avoid random splits and use the most recent period as the test set.

Compare filter, wrapper and embedded feature selection methods.

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Filter methods score features independently of a model using statistics such as correlation, chi-square or mutual information; they are fast but ignore interactions. Wrapper methods like Recursive Feature Elimination train models on feature subsets; they are accurate but slow. Embedded methods select during training, such as L1 regularisation or tree feature importances. Embedded methods are a common practical middle ground.

What is polynomial regression and what risk does it carry?

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Polynomial regression adds powers and interaction terms of features, such as x squared and x1 times x2, so a linear model can fit curves. It is still linear in its coefficients. The main risk is overfitting at high degrees, plus wild behaviour outside the training range. Use cross-validation to choose the degree, scale features, and combine it with Ridge regularisation.

How do you make an ML experiment reproducible?