Related Topics Data Science Question 14 What is Naive Bayes and how does it work? Answer Introduction: In data science, Naive Bayes is a classification algorithm that is based on Bayes’ theorem, which is a fundamental concept in probability theory. The algorithm is commonly used for text classification tasks such as spam filtering, sentiment analysis, […]
Related Topics Data Science Question 13 What is the difference between linear and logistic regression? Answer Introduction: In data science, linear regression and logistic regression are two commonly used statistical models for predictive modeling. Both linear regression and logistic regression are used extensively in data science for predictive modeling tasks, and can be implemented using
Related Topics Data Science Question 12 What is the curse of dimensionality? Answer Introduction : The curse of dimensionality in data science refers to the difficulty of processing and analyzing data as the number of features or dimensions increases. In high-dimensional data, the number of possible combinations of features can grow exponentially, which can lead to
Related Topics Data Science Question 11 What is a false positive and false negative? Answer Introduction: In statistics and machine learning, a false positive and false negative are both types of errors that can occur in a binary classification problem. False Positive: A false positive is an error that occurs when a model predicts a
Related Topics Data Science Question 10 What is the difference between a Type I and Type II error? Answer In data science, Type I and Type II errors refer to errors that can occur in hypothesis testing or statistical inference. Introduction: A Type I error, also known as a false positive, occurs when a null
Related Topics Data Science Question 9 What is gradient descent and how does it work? Answer Introduction: In data science, the term “gradient” usually refers to the gradient of a cost function with respect to the model parameters in a machine learning algorithm. The gradient of the cost function is a vector of partial derivatives
Related Topics Data Science Question 8 What is the difference between a decision tree and random forest? Answer Decision Tree: A decision tree is a type of supervised learning algorithm used for both classification and regression tasks. It works by breaking down a dataset into smaller and smaller subsets while at the same time an
Related Topics Data Science Question 7 What is regularization and how does it help prevent overfitting? Answer Introduction: Regularization is a technique used in machine learning to prevent overfitting by adding a penalty term to the loss function that the model is optimizing. The penalty term adds a constraint to the model that encourages it to
Related Topics Data Science Question 6 What is overfitting and how can it be avoided? Answer Introduction: Overfitting is a common problem in machine learning where a model is too complex and fits the training data too closely, resulting in poor performance on new, unseen data. In other words, the model has learned the training
Related Topics Data Science Question 5 What is the difference between supervised and unsupervised learning? Answer At first we have to know what is supervised and unsupervised learning? so, Supervised and unsupervised learning are two primary types of machine learning algorithms used to train models to make predictions or identify patterns in data. Supervised learning
