Prepare to ace your next machine learning interview with these top 25 questions and detailed answers that will set you apart from other candidates.
Machine learning (ML) is a subset of artificial intelligence where machines learn patterns from data without being explicitly programmed. Unlike traditional programming, where logic is manually coded, ML enables systems to learn and improve from experience.
Classification is about predicting discrete labels or categories (e.g., spam or not). Regression is about predicting continuous values (e.g., house price).
It refers to the problem where increasing the number of features makes the data sparse, degrading the model's performance due to difficulty in calculating distances and relationships.
Precision is the ratio of correctly predicted positives to total predicted positives. Recall is the ratio of correctly predicted positives to all actual positives. High precision means fewer false positives, while high recall means fewer false negatives.
Cross-validation splits the dataset into training and testing subsets multiple times to ensure that the model performs well across different data portions and avoids overfitting.
A confusion matrix is a performance measurement tool for classification problems. It shows the counts of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN). This helps in evaluating the accuracy, precision, recall, and F1 score of the model. It provides deeper insight into how well the model is predicting each class.
Feature engineering involves creating new features or transforming existing ones to improve model accuracy. It helps models learn better patterns and can significantly boost performance.
PCA is a dimensionality reduction technique that transforms features into a set of linearly uncorrelated components, preserving as much variance as possible.
Hyperparameters are configuration settings set before training (e.g., learning rate, depth). They're tuned using techniques like Grid Search, Random Search, or Bayesian Optimization to find optimal values for best performance.
The purpose of regularization is to prevent overfitting by discouraging overly complex models. It adds a penalty term to the loss function, shrinking large coefficient values. This helps the model generalize better to unseen data. Common techniques include L1 (Lasso) and L2 (Ridge) regularization.
The ROC (Receiver Operating Characteristic) curve plots the True Positive Rate vs. False Positive Rate. AUC (Area Under Curve) closer to 1 indicates a good classifier.
Gradient Descent is an optimization algorithm that minimizes loss by iteratively updating parameters in the direction of the steepest descent (negative gradient).
The learning rate controls how much weights are adjusted during training. Too high can overshoot minima; too low can lead to slow convergence.
Naive Bayes is a probabilistic classifier based on Bayes’ Theorem with strong independence assumptions. It is effective for text classification problems like spam detection.
K-means clustering is an unsupervised learning algorithm used to group data into K distinct clusters based on feature similarity. It assigns each data point to the nearest cluster centroid and updates centroids iteratively. The goal is to minimize intra-cluster variance and maximize separation between clusters. It is widely used in customer segmentation, image compression, and pattern recognition.
Model drift happens when the statistical properties of input data change over time, degrading model performance. It is addressed by monitoring models and retraining with recent data.