FAQ on Machine Learning

Both classification and regression are associated with prediction. Classification involves the identification of values or entities that lie in a specific group. The regression method, on the other hand, entails predicting a response value from a consecutive set of outcomes. The classification method is chosen over regression when the output of the model needs to … Read more

Type I Error: Type I error (False Positive) is an error where the outcome of a test shows the non-acceptance of a true condition. For example, a cricket match is going on and, when a batsman is not out, the umpire declares that he is out. This is a false positive condition. Here, the test … Read more

True Positive (TP): When the Machine Learning model correctly predicts the condition, it is said to have a True Positive value. True Negative (TN): When the Machine Learning model correctly predicts the negative condition or class, then it is said to have a True Negative value. False Positive (FP): When the Machine Learning model incorrectly … Read more

Variance Inflation Factor (VIF) is the estimate of the volume of multicollinearity in a collection of many regression variables. VIF = Variance of the model / Variance of the model with a single independent variable We have to calculate this ratio for every independent variable. If VIF is high, then it shows the high collinearity … Read more

To identify the Machine Learning algorithm for our problem, we should follow the below steps: Step 1: Problem Classification: Classification of the problem depends on the classification of input and output: Classifying the input: Classification of the input depends on whether we have data labeled (supervised learning) or unlabeled (unsupervised learning), or whether we have … Read more