ROC AUC explained
Evaluating classification models It is common in predictive modeling to try out a number of different models, apply each to a holdout sample (also called a... »
K-Means Clustering explained
Clustering Clustering is an unsupervised learning technique used for data classification. Unsupervised learning means there is no output variables to guide the learning process(no this or... »
Principal Component Analysis (PCA)
What is Principal Component Analysis? Principal component analysis, or PCA, is a method that rotates the dataset in a way such that the rotated features are... »
What Is Cross Validation ?
Cross-validation There is always a need to validate the stability of machine learning model, how well our model generalises to new, unseen data. Cross-validation is statistical... »
How to tune Random Forest model
Random Forest Random forest is an ensemble tool which takes a subset of observations and a subset of variation to build a decision tree.It builds multiple... »