Interview Questions on Machine Learning
Question 1: What is Machine Learning?
Answer: Machine Learning is a subset of artificial intelligence that focuses on developing algorithms and models that enable computers to learn from and make predictions or decisions based on data without explicit programming.
Question 2: Explain the difference between supervised and unsupervised learning.
Answer: Supervised learning involves training a model on a labeled dataset, where the algorithm learns to map input data to the correct output. Unsupervised learning, on the other hand, involves training on an unlabeled dataset, and the algorithm discovers patterns or structures within the data without explicit output labels.
Question 3: What is overfitting, and how can it be mitigated?
Answer: Overfitting occurs when a machine learning model performs well on the training data but poorly on new, unseen data due to capturing noise and not generalizing well. To mitigate overfitting, techniques like cross-validation, regularization, and increasing the amount of training data can be used.
Question 4: Explain the bias-variance trade-off.
Answer: The bias-variance trade-off refers to the balance between a model's ability to capture the underlying patterns in the data (low bias) and its ability to generalize to new data (low variance). Increasing model complexity often reduces bias but increases variance, and vice versa. The goal is to find the optimal trade-off for the specific problem.
Question 5: What are precision and recall?
Answer: Precision is the ratio of correctly predicted positive observations to the total predicted positive observations, while recall is the ratio of correctly predicted positive observations to the total actual positive observations. They are used to evaluate the performance of classification models, especially in cases where class imbalance exists.
Question 6: What is cross-validation?
Answer: Cross-validation is a technique used to evaluate the performance of a machine learning model by dividing the dataset into multiple subsets (folds). The model is trained on some folds and tested on others, rotating through all folds. This helps assess the model's generalization performance and reduces the risk of bias in evaluation.
Question 7: What is the purpose of feature engineering?
Answer: Feature engineering involves selecting, transforming, or creating new features from the raw data to improve the performance of machine learning models. Good feature engineering can lead to better model accuracy, reduced overfitting, and improved understanding of the underlying data patterns.
Question 8: Explain the concept of gradient descent.
Answer: Gradient descent is an optimization algorithm used to minimize the loss function in machine learning models. It involves iteratively updating the model's parameters in the opposite direction of the gradient of the loss function, aiming to find the minimum value of the loss function and thus improve the model's performance.
Question 9: What are ensemble methods?
Answer: Ensemble methods combine predictions from multiple base models to create a stronger overall prediction. Examples include Random Forest (combining decision trees), Gradient Boosting (building models sequentially), and Bagging (combining models trained on different subsets of data).
Question 10: What is deep learning?
Answer: Deep Learning is a subfield of Machine Learning that focuses on neural networks with multiple layers (deep neural networks). These networks can automatically learn to represent data at different levels of abstraction, enabling them to solve complex tasks like image and speech recognition.