Top 30 Machine Learning Interview Questions for Software Engineers

In a 2023 FAANG interview reported on Reddit, a candidate was asked to plot this tradeoff. Statistics show that 70% of ML failures stem from overfitting, per a Kaggle survey. If you’re prepping for coding interviews, our crash course can help implement these in Python.

What is Overfitting, and How Can You Prevent It?

Overfitting occurs when a model learns the training data too well, including noise and outliers, leading to poor performance on new data. Signs include high training accuracy but low validation accuracy.

Prevention strategies:

• Data Augmentation: Increase dataset size by transforming images or text.
• Early Stopping: Halt training when validation loss starts increasing.
• Dropout in Neural Networks: Randomly ignore neurons during training to prevent co-dependency.
• Cross-Validation: Use k-fold to ensure robust evaluation.

A real interview question from Meta: “How would you detect overfitting in a time-series model?” Answer: Monitor for erratic predictions on holdout sets. For web-based ML apps, integrate this knowledge with our web development course.

Differentiate Between Supervised and Unsupervised Learning.

Supervised learning uses labeled data to train models for prediction or classification (e.g., regression for house prices, classification for email spam). Unsupervised learning finds hidden patterns in unlabeled data (e.g., clustering customers by behavior, dimensionality reduction like PCA).

Pros/Cons:

• Supervised: Accurate but requires labeled data (expensive).
• Unsupervised: Explores data but harder to evaluate.

In Amazon interviews, this often ties into recommendation systems. Expert quote from Andrew Ng: “Unsupervised learning is key for big data exploration.” Brush up on implementations in our master DSA, web dev, system design course.

What is a Confusion Matrix, and Why is it Useful?

A confusion matrix is a table that evaluates classification model performance by comparing predicted vs. actual labels. For binary classification: True Positives (TP), True Negatives (TN), False Positives (FP), False Negatives (FN).

Metrics derived:

• Accuracy: (TP + TN) / Total
• Precision: TP / (TP + FP) – Minimizes false alarms.
• Recall: TP / (TP + FN) – Catches all positives.
• F1 Score: Harmonic mean of precision and recall.

Useful for imbalanced datasets, like fraud detection where positives are rare. In a 2024 Glassdoor review for Google, this was asked with a follow-up on ROC curves.

Supervised Learning Algorithms

Dive deeper into algorithms that predict outcomes based on labeled data.

Explain Linear Regression and its Assumptions.

Linear regression models the relationship between dependent and independent variables as y = mx + b. It minimizes mean squared error (MSE) using methods like gradient descent.

Assumptions:

1. Linearity: Relationship is linear.
2. Independence: Observations are independent.
3. Homoscedasticity: Constant variance of errors.
4. Normality: Errors are normally distributed.

Violations? Use transformations or switch to nonlinear models. Real interview at Microsoft: “How to handle multicollinearity?” Answer: VIF or ridge regression.

What is Logistic Regression? When Would You Use It?

Logistic regression is for binary classification, using the sigmoid function to output probabilities between 0 and 1. Formula: p = 1 / (1 + e^(-z)), where z is linear combination.

Use cases: Spam detection, medical diagnosis. Unlike linear regression, it handles categorical outcomes. Prevent overfitting with L1 regularization for feature selection.

From FAANG interviews: “Derive the cost function.” It’s cross-entropy loss.

Describe Decision Trees and How They Work.

Decision trees split data based on features to maximize information gain (entropy reduction) or Gini impurity. Root node starts, leaves are predictions.

Pros: Interpretable, handles nonlinear data.

Cons: Prone to overfitting—prune or use random forests.

In Snap interviews, asked: “How to handle categorical variables?” Answer: One-hot encoding.