Classification
Random Forest
Random Forest Description Random Forest is a Supervised Machine Learning algorithm. It works on the Bagging (Bootstrap Aggregation) principle of the Ensemble technique. Thus, it uses multiple models instead of a single model to make predictions. It ...
Gradient Boosting in Classification
Gradient Boosting in Classification Description Gradient boosting is a machine learning algorithm. It is a learning method that combines multiple predictive models like decision tree to create a strong predictive model. Why use High Predictive ...
Extreme Gradient Boost Classification
Extreme Gradient Boost Classification Description Extreme Gradient Boost (XGBoost) is a Decision Tree-based ensemble algorithm. XGBoost uses a gradient boosting framework. It approaches the process of sequential tree building using parallelized ...
Decision Tree
Decision Tree Description Decision Tree builds a classification model in the form of a tree structure where each leaf node represented a class or a decision. Why to use Data Classification When to use When the data is categorized into Boolean values. ...
Categorical Naive Bayes
Categorical Naive Bayes Description The categorical Naïve Bayes algorithm is suitable for categorically discrete values like Weather Prediction, and Medical Diagnosis. It is the simplest and fastest classification algorithm. Why to use It is the ...
Binomial Logistic Regression
Binomial Logistic Regression Description Binomial Logistic Regression predicts the probability that an observation falls into one of the two categories of the binary dependent variable, based on one or more, categorical or continuous independent ...
AdaBoost in Classification
AdaBoost in Classification Description AdaBoost is a technique in Machine Learning used as an Ensemble Method. AdaBoost is a boosting algorithm that combines the predictions of multiple weak classifiers to create a strong classifier. Why to use ...
Classification
Classification is the process of predicting the class of given data points. Classes are referred to as targets/ labels or categories. Classification predictive modeling is the task of approximating a mapping function (f) from input variables (X) to ...