Rapid Fire on Machine Learning

Multi collinearity can be dealt with by the following steps: Remove highly correlated predictors from the model. Use Partial Least Squares Regression (PLS) or Principal Components Analysis,   Dealing with multicollinearity in machine learning is crucial to ensure the stability and reliability of your model. Multicollinearity occurs when independent variables in a regression model are … Read more

The most important features which one can tune in decision trees are: Splitting criteria Min_leaves Min_samples Max_depth In machine learning interviews, when asked about hyper-parameters of decision trees, you can mention several key hyper-parameters that are commonly used to tune and optimize the performance of decision trees. Some of these include: Maximum Depth (max_depth): This … Read more

Classifier penalty, classifier solver and classifier C are the trainable hyperparameters of a Logistic Regression Classifier. These can be specified exclusively with values in Grid Search to hyper tune a Logistic Classifier.   In a logistic regression model, hyperparameters are the external configuration settings that are not learned from the data but are set prior … Read more

No, logistic regression cannot be used for classes more than 2 as it is a binary classifier. For multi-class classification algorithms like Decision Trees, Naïve Bayes’ Classifiers are better suited.   Yes, logistic regression can be used for classes more than two, but it is typically used for binary classification problems where the outcome variable … Read more

The p-value gives the probability of the null hypothesis is true. It gives us the statistical significance of our results. In other words, p-value determines the confidence of a model in a particular output.   In the context of machine learning interview questions, the p-value is often associated with statistical hypothesis testing rather than traditional … Read more