Machine Learning Technical Round Questions &amp; Answers

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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.

What should you monitor for a model in production?

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Monitor service health such as latency, error rate and throughput; input data quality such as missing values, schema changes and out-of-range values; feature and prediction distribution drift; and real performance once labels arrive. Also track business KPIs the model influences. Set alerts with thresholds and owners, and keep a dashboard so issues are found before users report them.

Explain hierarchical clustering and dendrograms.

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Agglomerative hierarchical clustering starts with each point as its own cluster and repeatedly merges the two closest clusters. Linkage defines closeness: single, complete, average or Ward, which minimises variance. A dendrogram shows the merge history, and cutting it at a height gives the clusters. It does not need k upfront but scales poorly, roughly O(n squared) memory, so it suits smaller datasets.

What is PCA and how does it work?

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Principal Component Analysis finds new orthogonal axes, the principal components, that capture maximum variance in the data. It works by computing eigenvectors of the covariance matrix, or via SVD, after centring and usually standardising the data. Keeping the top components that explain, say, 95 percent of variance reduces dimensions, removes correlation and speeds up models, but the components are harder to interpret.

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.

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

Classification ModelsMedium

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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.

A product team wants 'similar products' recommendations with no purchase labels. How would you start?

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I would build product embeddings from content: TF-IDF or sentence embeddings of titles and descriptions, plus category and price features. Then use cosine similarity with nearest-neighbour search, such as scikit-learn NearestNeighbors or an approximate index like FAISS for large catalogues. I would review samples with the product team and later add co-view or co-purchase signals once collected.

How do you choose a classification threshold that maximises F1?

Classification ModelsMedium

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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.

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.

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

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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.

Write a unit test for a model prediction function.

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import joblib, pandas as pd def test_predict_shape_and_range(): model = joblib.load('model_v1.joblib') df = pd.DataFrame([{'age': 30, 'income': 50000.0, 'city': 'Pune'}, {'age': 45, 'income': 0.0, 'city': 'UnknownCity'}]) p = model.predict_proba(df)[:, 1] assert p.shape == (2,) assert ((p >= 0) & (p <= 1)).all() Run with pytest in CI so broken artifacts never reach production.

What is DBSCAN and when is it better than k-means?

Data Preprocessing and Feature EngineeringEasy

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DBSCAN groups points that lie in dense regions, defined by eps (neighbourhood radius) and min_samples, and labels points in sparse areas as noise. It finds arbitrarily shaped clusters, does not need k, and handles outliers. It struggles when clusters have very different densities and in high dimensions. HDBSCAN improves on it for varying densities.

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

Model Evaluation and TuningMedium

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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.

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

Model Evaluation and TuningHard

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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.

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.

Tune LightGBM with Optuna.

Model Evaluation and TuningHard

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import optuna from sklearn.model_selection import cross_val_score def objective(trial): p = {'num_leaves': trial.suggest_int('num_leaves', 15, 255), 'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.2, log=True), 'min_child_samples': trial.suggest_int('min_child_samples', 10, 200)} m = lgb.LGBMClassifier(n_estimators=500, **p) return cross_val_score(m, X, y, cv=5, scoring='roc_auc').mean() study = optuna.create_study(direction='maximize') study.optimize(objective, n_trials=50)

How do you tune the alpha of Ridge regression using cross-validation?

Regression ModelsMedium

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RidgeCV tests many alphas efficiently; scale features first. from sklearn.linear_model import RidgeCV from sklearn.pipeline import make_pipeline from sklearn.preprocessing import StandardScaler import numpy as np m = make_pipeline(StandardScaler(), RidgeCV(alphas=np.logspace(-3, 3, 30))) m.fit(X_train, y_train) print(m[-1].alpha_) LassoCV and ElasticNetCV work the same way.

How do you handle missing values in a dataset?

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First understand why values are missing: completely at random, at random, or not at random. Options are dropping rows or columns with heavy missingness, simple imputation with mean, median or mode, model-based imputation like KNNImputer or IterativeImputer, or a separate 'missing' category. Adding a missing-indicator column often helps because missingness itself can be predictive, such as income left blank.

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

Model Evaluation and TuningMedium

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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.

Which hyperparameters matter most when tuning a gradient boosting model?

Decision Trees and EnsemblesHard

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The key ones are learning_rate and number of trees, tuned together with early stopping; tree complexity through max_depth or num_leaves and min_child_samples; and randomness through subsample and colsample_bytree. Regularisation terms like lambda and alpha help on noisy data. A common approach is to fix a small learning rate such as 0.05, use early stopping for trees, then tune complexity.

What is multicollinearity and how do you detect it?

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Multicollinearity is high correlation between input features, which makes linear model coefficients unstable and hard to interpret, though predictions may remain fine. Detect it with a correlation matrix or the Variance Inflation Factor, where VIF above 5 to 10 signals a problem. Fix it by dropping one of the correlated features, combining them, using PCA, or applying Ridge regularisation.

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 difference between batch, stochastic and mini-batch gradient descent?

ML FundamentalsMedium

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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.

A credit model's probability of 0.3 is used to set interest rates, but defaults in that band are 12 percent. What is wrong?

Classification ModelsHard

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The model is poorly calibrated: it ranks customers but its probabilities are not reliable. Since pricing depends on actual probabilities, I would calibrate it with isotonic regression or Platt scaling on a held-out set and validate with a reliability curve and Brier score. I would also check whether the default rate has drifted since training, which would require retraining.

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

Regression ModelsMedium

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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.

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

Classification ModelsMedium

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One-vs-rest trains one binary classifier per class against all others and picks the highest score; it is simple but probabilities may not sum to one. Multinomial (softmax) logistic regression models all classes jointly with a single loss, giving consistent probabilities. For metrics, use macro averaging to treat classes equally or weighted averaging to account for class frequency.

k-means gives one huge cluster and several tiny ones. What could be wrong?

Clustering and Dimensionality ReductionHard

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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.

Run DBSCAN and count clusters and noise points.

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from sklearn.cluster import DBSCAN import numpy as np db = DBSCAN(eps=0.5, min_samples=10).fit(Xs) labels = db.labels_ n_clusters = len(set(labels)) - (1 if -1 in labels else 0) print(n_clusters, 'clusters,', np.sum(labels == -1), 'noise') Pick eps from the knee of a sorted k-nearest-neighbour distance plot.

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 a model registry and why is it useful?

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A model registry is a central store of model versions with metadata: training data version, parameters, metrics, owner and stage such as staging or production. It makes promotions and rollbacks controlled and auditable, and lets serving systems load a model by name and version. MLflow Model Registry, SageMaker Model Registry and Vertex AI Model Registry are common examples.

How do you detect and treat outliers?

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Detect with box plots, the IQR rule (below Q1 - 1.5 IQR or above Q3 + 1.5 IQR), z-scores above 3, or models like Isolation Forest. Treatment depends on cause: fix data entry errors, cap values (winsorise), apply log transforms for skewed data, or use robust models and losses. Genuine extreme values, such as large fraud amounts, may be the most important signal and should not be removed blindly.

How do you apply SMOTE correctly without leakage?

Classification ModelsHard

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Use an imbalanced-learn pipeline so oversampling happens only on training folds. from imblearn.pipeline import Pipeline from imblearn.over_sampling import SMOTE from sklearn.model_selection import cross_val_score pipe = Pipeline([('pre', pre), ('smote', SMOTE(random_state=42)), ('clf', LogisticRegression(max_iter=1000))]) print(cross_val_score(pipe, X, y, cv=5, scoring='average_precision').mean()) Applying SMOTE before splitting puts synthetic copies of test points into training.

Detect anomalies with Isolation Forest.

Data Preprocessing and Feature EngineeringEasy

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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.

Explain gradient descent and the role of the learning rate.

ML FundamentalsMedium

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Gradient descent minimises a loss by repeatedly moving parameters in the direction of the negative gradient: w = w - learning_rate * gradient. The learning rate controls step size. Too high and the loss oscillates or diverges; too low and training is very slow or gets stuck. Variants include batch, stochastic and mini-batch gradient descent, plus adaptive optimisers like Adam.

What is feature engineering and why does it matter?

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Feature engineering is creating input variables that make patterns easier for a model to learn, using domain knowledge. Examples are extracting day of week from a date, ratios like debt-to-income, rolling averages of past purchases, or text length. On tabular problems good features often improve results more than switching algorithms. Features must use only information available at prediction time.

Build RFM features for customer segmentation in pandas.

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snap = df['order_date'].max() + pd.Timedelta(days=1) rfm = df.groupby('customer_id').agg( recency=('order_date', lambda d: (snap - d.max()).days), frequency=('order_id', 'nunique'), monetary=('amount', 'sum')) rfm[['frequency', 'monetary']] = np.log1p(rfm[['frequency', 'monetary']]) Scale these columns before clustering.

Why can impurity-based feature importance be misleading?

Decision Trees and EnsemblesHard

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Impurity-based importance is computed on training data and favours high-cardinality or continuous features, which offer more split points, even if they are noise. It also splits credit between correlated features. Permutation importance on validation data, which measures the score drop when a feature is shuffled, is more reliable, and SHAP values give consistent per-prediction attributions.

What makes XGBoost, LightGBM and CatBoost different?

Decision Trees and EnsemblesMedium

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XGBoost adds regularisation to the objective, uses second-order gradients and handles missing values natively. LightGBM grows trees leaf-wise and uses histogram binning, making it very fast on large data, though it can overfit small data. CatBoost handles categorical features with ordered target statistics and uses symmetric trees, giving strong defaults with less tuning. All three support GPU training.

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