Q1. What is cross-validation? Cross-validation is essentially a technique used to assess how well a model performs on a new independent dataset. The simplest example of cross-validation is when you split your data into two groups: training data and testing data, where you use the training data to build the model and the testing data to test the model. Q2. What is the distribution of the target variable? There are a number of metrics that can be used, including adjusted r-squared, MAE, MSE, accuracy, recall, precision, f1 score, and the list goes on. Q3. What does NLP stand for? NLP stands for Natural Language Processing. It is a branch of artificial intelligence that gives machines the ability to read and understand human languages. Q4. What is the significance of Sampling? Name some techniques for Sampling? Answer : For analyzing the data we cannot proceed with the whole volume at once for large datasets. We need to take some samples from the data which can represent the...
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