Data Science – How XGBoost Algorithm Works ? (1) Difference Between Training & Testing Set? (2) Difference In Validation Set & Testing Set? (3) Define Bias & Variance. (4) How You Will Handle Missing Values In The Dataset ? Mean, Median, Mode KNN Imputation, MICE Imputation, Regression Imputation. Forward Fill, Backward Fill, Interpolation. (5) How Decision Tree Classifier Works ? (6) How Logistic Regression Model Evaluated? (7) Assumptions Of Linear Regression Model. Linearity. Multicollinearity. Normality. Homoscedasticity.  No Autocorrelation. (8) What Is Multicollinearity How To Handle It? (9) Explain Why Performance Of XGBoost Is Better & Why ? (10) Why Is

Read More