In this Machine Learning Basic or Core Interview questions, I will cover the most frequently asked questions along with answers and links to the article to learn more in detail. When you are looking for a job in Machine Learning it’s always good to have in-depth knowledge of the subject and I hope SparkByExamples.com provides you with the required knowledge to crack the interview.
Note: This Machine Learning Interview questions page is in progress. hence, I will keep adding new questions to this page. If you are looking for an answer to any question that I have not answered yet, please ask in a comment. I will try to reply within a day or so.
1. What is Machine Learning?
Answer: Machine learning is the process of training computers to recognize patterns in data, so that they can make predictions or take actions based on new data that they have not seen before.
2. What is the difference between supervised and unsupervised learning?
Answer: Supervised learning involves training a model on labeled data, where the desired output is known, in order to make predictions on new data. Unsupervised learning involves training a model on unlabeled data, where the desired output is not known, in order to identify patterns or groupings in the data.
3. What is overfitting in Machine Learning?
Answer: Overfitting occurs when a model is trained too well on the training data, to the point that it starts to fit the noise in the data rather than the underlying pattern. This can result in poor performance on new data.
4. What is the curse of dimensionality and how can it be addressed?
Answer: The curse of dimensionality refers to the difficulty of analyzing data with a large number of features or dimensions. This can lead to issues with overfitting and poor performance. One way to address this is by using feature selection techniques to reduce the number of features or by using dimensionality reduction techniques such as PCA.
5. What are the different types of kernels in Support Vector Machines (SVM)?
Answer: Some of the commonly used kernels in SVMs include linear, polynomial, radial basis function (RBF), and sigmoid kernels.
6. What is regularization and why is it important in Machine Learning?
Answer: Regularization is a technique used to prevent overfitting in machine learning by adding a penalty term to the loss function to discourage large weights. It is important because it can help to improve the generalization performance of a model.
7. What is cross-validation and why is it important?
Answer: Cross-validation is a technique used to evaluate the performance of a model by partitioning the data into training and validation sets multiple times. It is important because it can help to estimate the generalization performance of a model and prevent overfitting.
8. What is the difference between a validation set and a test set?
Answer: A validation set is used to tune the hyperparameters of a model during the training process, while a test set is used to evaluate the final performance of the model after it has been trained.
9. What is the ROC curve and what is it used for?
Answer: The ROC (Receiver Operating Characteristic) curve is a graphical plot that shows the performance of a binary classifier system as the discrimination threshold is varied. It is a two-dimensional plot that shows the relationship between the True Positive Rate (TPR) and the False Positive Rate (FPR) of a binary classifier at different threshold settings. It is used to evaluate the performance of a binary classification model.
10. What is the precision-recall curve and what is it used for?
Answer: The precision-recall curve is a plot of precision (positive predictive value) against recall (true positive rate) at various threshold settings. It is used to evaluate the performance of a binary classification model, particularly in cases where the classes are imbalanced.
11. What is bias-variance tradeoff and how do you balance it?
Answer: The bias-variance tradeoff refers to the tradeoff between the complexity of a model and its ability to generalize to new data. A model with high bias will underfit the data, while a model with high variance will overfit the data. Balancing this tradeoff involves finding the right level of complexity for the model and using techniques such as regularization to prevent overfitting.
12. What is gradient descent and how does it work?
Answer: Gradient descent is an optimization algorithm used to minimize the loss function of a model by iteratively adjusting the weights of the model in the direction of the steepest descent of the gradient. The gradient is the vector of partial derivatives of the loss function with respect to the weights, and it indicates the direction of the maximum increase of the function.
13. What is a decision tree and how does it work?
Answer: A decision tree is a model used for classification or regression that consists of a tree-like structure where each node represents a feature or attribute, each branch represents a decision rule based on that feature, and each leaf represents a class or output value. The tree is constructed recursively by choosing the feature that best splits the data at each node, based on some criterion such as information gain or Gini impurity.
14. What is the difference between Bagging and Boosting?
Answer: Bagging (Bootstrap Aggregating) and Boosting are ensemble learning methods that combine multiple models to improve performance. Bagging involves training multiple independent models on different bootstrap samples of the training data and then aggregating their predictions, typically by majority voting for classification or averaging for regression. Boosting involves training a sequence of models, each one focusing on the examples that were misclassified by the previous model, and then combining their predictions, typically by weighted averaging.
15. What is the difference between Random Forest and Gradient Boosting?
Answer: Random Forest and Gradient Boosting are both ensemble learning methods that use decision trees as base models, but they differ in how they combine the trees. Random Forest combines multiple independent trees trained on different subsets of the features and the training data, while Gradient Boosting combines multiple trees sequentially by iteratively fitting each new tree to the negative gradient of the loss function with respect to the previous predictions.
16. What is PCA and what is it used for?
Answer: PCA (Principal Component Analysis) is a technique used for dimensionality reduction that transforms high-dimensional data into a lower-dimensional space while retaining most of the variance in the data. It does this by finding the linear combinations of the original features that capture the most variation in the data, and then projecting the data onto those combinations.
17. What is K-Means clustering and how does it work?
Answer: This is one of the most asked interview questions in machine learning, K-Means clustering is an unsupervised learning method used for clustering or grouping similar data points together. It works by randomly selecting K initial centroids and then assigning each data point to the nearest centroid based on the distance metric, typically Euclidean distance. It then recalculates the centroids based on the mean of the points assigned to each cluster and repeats the process until convergence.
18. What is Naive Bayes and how does it work?
Answer: Naive Bayes is a probabilistic model used for classification that assumes that the features are conditionally independent given the class. It works by calculating the posterior probability of each class given the observed features, using Bayes’ theorem and the likelihood of the features given the class, and then choosing the class with the highest probability.
19. What is the difference between L1 and L2 regularization?
Answer: L1 and L2 regularization are two common techniques used to prevent overfitting in machine learning models. L1 regularization adds a penalty term proportional to the absolute value of the weights, which tends to produce sparse models with many zero weights. L2 regularization adds a penalty term proportional to the square of the weights, which tends to produce models with smaller, smoother weights.
Conclusion
In conclusion, these are the most asked machine learning interview questions. I hope this helps you to crack your interview in machine learning and advance your career in ML.
