Decision Trees Modeling & Supervised Learning using R

Name: Decision Trees Modeling & Supervised Learning using R
Rating: 4.6 (3 reviews)

Learn Decision Trees Modeling using R in a simple way

Created byEDUCBA Bridging the Gap

Last updated 11/2023

English

English [Auto],

What you'll learn

This course includes learning decision tree modeling which are used by data scientists or people who aspire to be the data scientist
Decision Tree Regression
Decision Tree Theory
Implementation of Decision Tree Classifications using R

Course content

4 sections • 14 lectures • 1h 38m total length

Introduction to Decision Trees7:30
Explore the theory and practical implementation of decision trees in R, covering regression and classification models, and see real-world applications from credit scoring to medical diagnosis.
Route Node7:58
Explore the dataset play dot xls and build decision trees to predict play or not, from root nodes to leaves, using entropy and information gain via ID3.
Route Node Continue11:03
Learn to model decisions with decision trees in R for supervised learning, covering entropy, information gain, root node selection, ID3 and CART, and practical splits for classification and regression tasks.

Advertisement Dataset7:12
learn to implement decision trees in R with the advertisement dataset. import and prep data using read.csv, then examine gender, age, salary, and purchased for modeling.
Data Preprocessing9:29
Select required variables and encode the purchased variable as a factor for classification; install and load ktools, set a seed, and split data into training and test sets at 0.75.
Feature Scaling8:07
Explore feature scaling for distance-based algorithms using Euclidean distance, applying standardization or normalization to balance age and salary in training and test sets.
Classifier - rpart9:04
Learn to build a classification model in R with the rpart package, fit a CART classifier, generate predictions, and evaluate performance with a confusion matrix and accuracy.
Confusion Matrix7:27
Learn to compute a confusion matrix in R to assess accuracy, sensitivity, and specificity, interpret true positives and negatives, and plot a decision tree with practical example data.

Diabetes Dataset4:11
Build a decision tree on the diabetes dataset from the UCI repository, with 768 observations across nine variables, to predict is_diabetic. Split data 70/30, train, plot, and assess.
Plot Model-Classifier7:12
Plot a decision tree classifier in R using traditional methods and the part dot plot package, then generate predictions and prepare for a confusion matrix.
Prediction2:33
Build a confusion matrix in R for a decision tree, evaluate accuracy with caret, and tune control parameters to manage overfitting while noting the next set covers regression.

Caeseats Dataset5:30
Explore applying decision tree regression to the car seats dataset in R, loading caret, inspecting 11 variables, and performing a 75% train-test split to predict sales.
Split7:33
Split dataset into training and test sets and build a regression model with the R part and the tree package, then evaluate with mean squared error.
Tree Package3:21
Install and compare the trees package in R to plot decision trees, run regressor variants, and compare regression and classification results.

Requirements

No prior knowledge of machine learning required
Basics of R

Description

The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data. This course includes learning decision tree modeling which are used by data scientists or people who inspire to be the data scientist. The tutorials will include the following;

Decision Tree Theory
Implementation using R Decision Tree Classification
Decision Tree Regression

Decision Tree in R is a machine-learning algorithm that can be a classification or regression tree analysis. The decision tree can be represented by graphical representation as a tree with leaves and branches structure. The leaves are generally the data points and branches are the condition to make decisions for the class of data set. Decision trees in R are considered as supervised Machine learning models as possible outcomes of the decision points are well defined for the data set. It is also known as the CART model or Classification and Regression Trees. There is a popular R package known as rpart which is used to create the decision trees in R.

Who this course is for:

Anyone who wants to learn about data and analytics
Data Engineers, Analysts, Architects, Software Engineers, IT operations, Technical managers

Decision Trees Modeling & Supervised Learning using R

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 27min

Advertisement Dataset5 lectures • 41min

Diabetes Dataset3 lectures • 14min

Caeseats Dataset3 lectures • 16min

Requirements

Description

Who this course is for: