Machine Learning Online Assessment Questions &amp; Answers

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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.

How does Isolation Forest detect anomalies?

Data Preprocessing and Feature EngineeringEasy

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Isolation Forest builds random trees that split on random features and random thresholds. Anomalies are few and different, so they get isolated in fewer splits, giving shorter average path lengths. The anomaly score comes from that path length. It is fast, works without labels and scales well. The contamination parameter sets the expected share of anomalies and determines the threshold.

How do you extract useful features from a datetime column in pandas?

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Convert to datetime and pull out calendar parts. import pandas as pd df['ts'] = pd.to_datetime(df['ts']) df['hour'] = df['ts'].dt.hour df['dow'] = df['ts'].dt.dayofweek df['month'] = df['ts'].dt.month df['is_weekend'] = df['dow'].isin([5, 6]).astype(int) For cyclical features like hour, sine and cosine encoding keeps 23 and 0 close together.

What is Naive Bayes and why is it called naive?

Classification ModelsEasy

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Naive Bayes applies Bayes' theorem to compute the probability of each class given the features, assuming features are conditionally independent given the class. That independence assumption is rarely true, hence 'naive', but the model still works well for text classification like spam detection. Variants include Gaussian, Multinomial for word counts and Bernoulli for binary features. It is fast and needs little data.

Run k-means and plot the elbow and silhouette scores.

Clustering and Dimensionality ReductionEasy

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from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score Xs = StandardScaler().fit_transform(X) for k in range(2, 10): km = KMeans(n_clusters=k, n_init=10, random_state=42).fit(Xs) print(k, round(km.inertia_), round(silhouette_score(Xs, km.labels_), 3)) Look for the bend in inertia and a high silhouette value.

Find the most similar items using cosine similarity.

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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.

How would you build a quick baseline before trying complex models?

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Use a DummyClassifier or DummyRegressor to see the score of a trivial guess, then a simple model. from sklearn.dummy import DummyClassifier base = DummyClassifier(strategy='most_frequent') base.fit(X_train, y_train) print(base.score(X_test, y_test)) Any real model must clearly beat this number; otherwise the features carry little signal.

What is the bias-variance trade-off?

ML FundamentalsMedium

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Bias is error from overly simple assumptions, which causes underfitting. Variance is error from sensitivity to the particular training sample, which causes overfitting. Increasing model complexity usually lowers bias but raises variance. The goal is the complexity that minimises total test error. For example, a linear model on curved data has high bias, while a very deep unpruned tree has high variance.

How would you encode cyclical features like hour of day or month?

Data Preprocessing and Feature EngineeringEasy

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Map the value onto a circle with sine and cosine so the end connects to the start. import numpy as np df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24) df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24) Now 23:00 and 00:00 are close, which linear and distance-based models cannot see with raw integers. Tree models often manage with the raw value.

How do you plot an ROC curve and get AUC in scikit-learn?

Classification ModelsEasy

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from sklearn.metrics import roc_auc_score, RocCurveDisplay import matplotlib.pyplot as plt proba = clf.predict_proba(X_test)[:, 1] print('AUC', roc_auc_score(y_test, proba)) RocCurveDisplay.from_predictions(y_test, proba) plt.show() Pass probabilities or decision scores, not hard labels, otherwise the curve has only one point.

Why is feature scaling needed and which algorithms need it?

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Scaling puts features on comparable ranges so that one large-valued feature does not dominate distances or gradients. Distance and gradient based models need it: k-NN, k-means, SVM, PCA, linear and logistic regression with regularisation, and neural networks. Tree-based models like random forest and XGBoost split on thresholds, so they are largely unaffected by scaling.

What is the difference between a regression and a classification problem?

Data Preprocessing and Feature EngineeringEasy

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Regression predicts a continuous number, such as sales next month or delivery time in minutes. Classification predicts a discrete category, such as fraud or not fraud, or one of five product types. The choice decides the loss function and metrics: regression uses MSE, MAE or R-squared, while classification uses log loss, accuracy, precision, recall or AUC.

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.

How do you find and remove duplicate rows and check missing values in pandas?

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Quick data-quality checks before modelling: print(df.isna().sum().sort_values(ascending=False)) print(df.duplicated().sum()) df = df.drop_duplicates() df['age'] = df['age'].fillna(df['age'].median()) For modelling, do the median imputation inside a pipeline instead so the value is learned only from training data.

A model's accuracy dropped 10 percent over three months in production. How do you investigate?

MLOps and DeploymentMedium

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I would first check data pipelines for broken features, nulls or schema changes, then compare current feature distributions with training data using PSI to find drift. I would check whether the label definition or business process changed, and analyse errors by segment. If drift is real, retrain on recent data, possibly with time-weighted samples, and set up automatic drift alerts and scheduled retraining.

What is data leakage? Give two examples.

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Data leakage is when information not available at prediction time gets into training, giving falsely high validation scores. Example one: fitting a scaler or imputer on the full dataset before splitting. Example two: using a feature created after the event, such as 'refund issued' when predicting fraud. Prevent it with pipelines, time-aware splits and checking each feature's timestamp.

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 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.

What is data drift and concept drift?

MLOps and DeploymentMedium

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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.

Build RFM features for customer segmentation in pandas.

Clustering and Dimensionality ReductionEasy

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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.

How do you choose the number of clusters k?

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Common methods are the elbow method, plotting inertia against k and looking for the bend; the silhouette score, which measures how close points are to their own cluster versus the next one; and the gap statistic. Business usability matters too: four clear segments a marketing team can act on are better than nine statistically optimal but confusing ones.

Write a unit test for a model prediction function.

MLOps and DeploymentMedium

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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.

Random forest and LightGBM give the same AUC. Which would you deploy?

Decision Trees and EnsemblesMedium

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I would compare beyond AUC: model size, inference latency, training time, calibration and stability across time periods. LightGBM is usually smaller and faster to score, while random forest needs less tuning and is less prone to overfitting. If both are equal, I would choose the one that is cheaper to run and easier for the team to maintain and retrain.

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.

Why do single decision trees overfit and how do you control it?

Decision Trees and EnsemblesEasy

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An unrestricted tree keeps splitting until leaves are pure, memorising noise. Control it with pre-pruning hyperparameters like max_depth, min_samples_split, min_samples_leaf and max_leaf_nodes, or with post-pruning through cost-complexity pruning using ccp_alpha. In practice ensembles such as random forests or boosting are used to reduce the variance of single trees.

Why containerise an ML model with Docker?

MLOps and DeploymentEasy

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Docker packages the model, code, Python version and exact library versions into one image that runs the same on a laptop, a test server or Kubernetes. This removes 'works on my machine' problems, especially with scikit-learn version mismatches. Images can be scanned, versioned and deployed through CI/CD, and scaled horizontally behind a load balancer on platforms like Kubernetes, ECS or Cloud Run.

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

Data Preprocessing and Feature EngineeringHard

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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.

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.

Use statsmodels to get p-values for regression coefficients.

Regression ModelsMedium

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import statsmodels.api as sm Xc = sm.add_constant(X_train) ols = sm.OLS(y_train, Xc).fit() print(ols.summary()) The summary shows coefficients, standard errors, p-values, confidence intervals, R-squared and diagnostics. scikit-learn focuses on prediction and does not report p-values, so statsmodels is preferred for inference.

Explain supervised, unsupervised and reinforcement learning with one example each.

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Supervised learning trains on labelled data, such as predicting house prices from past sales. Unsupervised learning finds structure in unlabelled data, such as grouping customers into segments with k-means. Reinforcement learning trains an agent through rewards and penalties from an environment, such as a program learning to play a game. Semi-supervised and self-supervised learning sit between these when labels are scarce.

Compute permutation importance on validation data.

Decision Trees and EnsemblesMedium

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from sklearn.inspection import permutation_importance import pandas as pd r = permutation_importance(model, X_val, y_val, n_repeats=10, scoring='roc_auc', random_state=42, n_jobs=-1) imp = pd.Series(r.importances_mean, index=X_val.columns) print(imp.sort_values(ascending=False)) Near-zero or negative values mean the feature adds little on unseen data.

How do you build a scikit-learn pipeline for mixed numeric and categorical data?

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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))])

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