Random Forest Algorithm Using Python

Learn Random Forest Algorithm using Python

What you'll learn

Through this training we are going to learn and apply how the random forest algorithm works

Improve the model Performance using Random Forest.

Build Random Forest Model on Training Data set.

Predict and Validate Performance of Model.

Requirements

Basic Machine learning concepts and Python

Description

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.hrough this training we are going to learn and apply how the random forest algorithm works and several other important things about it.The course includes the following;1) Extract the Data to the platform.2) Apply data Transformation.3) Bifurcate DatTa into Training and Testing Data set.4) Built Random Forest Model on Training Data set.5) Predict using Testing Data set.6) Validate the Model Performance.7) Improve the model Performance using Random Forest.8) Predict and Validate Performance of Model.Random forest in Python offers an accurate method of predicting results using subsets of data, split from global data set, using multi-various conditions, flowing through numerous decision trees using the available data on hand and provides a perfect unsupervised data model platform for both Classification or Regression cases as applicable; It handles high dimensional data without the need any pre-processing or transformation of the initial data and allows parallel processing for quicker results.The unique feature of Random forest is supervised learning. What it means is that data is segregated into multiple units based on conditions and formed as multiple decision trees. These decision trees have minimal randomness (low Entropy), neatly classified and labeled for structured data searches and validations. Little training is needed to make the data models active in various decision trees.

Overview

Section 1: Introduction

Lecture 1 Introduction and Understanding of SONAR Dataset

Section 2: Getting Started

Lecture 2 Load a CSV File

Lecture 3 Load a CSV File Continue

Lecture 4 Split a dataset into k Folds

Lecture 5 Evaluate an Algorithm using a Cross Validation Split

Lecture 6 Calculate the Gini index for a Split Dataset

Lecture 7 Select the Best Split Point for a Dataset

Section 3: Node Value and Subsample

Lecture 8 Create a Terminal Node Value

Lecture 9 Build a Decision Tree

Lecture 10 Create a Random Subsample

Lecture 11 Random Forest Algorithm

Lecture 12 Test the Random Forest Algorithm on Sonar Dataset

Lecture 13 Evaluate Algorithm

Aspiring Data Scientists,Artificial Intelligence/Machine Learning/ Engineers