What are the advantages of using decision tree?

Simple to comprehend and interpret. Little data preparation is required. The cost of using the tree (predicting data) is proportional to the quantity of data points needed to train the tree. Capable of dealing with both numerical and category data. Capable of dealing with multi-output difficulties.

Course Content

Introduction to Machine Learning

1m 5s

Environment Setup part 1

3m 26s

Environment Setup part 2

4m 43s

Environment Setup part 3

2m 33s

Quiz : Introduction to Machine Learning

Data Wrangling

12m 26s

Importing Libraries and Dataset

11m 19s

Handling Missing Data

11m 51s

Handling Missing Data - Practical

17m 3s

Encoding Categorical Data

6m 34s

Encoding Catergorical Data - Practical

8m 3s

Splitting Dataset

3m 38s

Splitting Dataset - Practical

9m 36s

Normalizing the Data - Part 1

6m 18s

Normalizing the Data - Part2

6m 58s

Finding Machine Learning Datasets

9m 17s

Exploratory Data Analysis

Plotting Graphs - Part 1

19m 12s

Plotting Graphs - Part 2

23m 44s

Distribution Models - Part 1

33m 54s

Distribution Models - Part 2

13m 56s

Assignment of Data Preprocessing for Machine Learning

1m 39s

Quiz : Data Preprocessing for Machine Learning

10m

Machine Learning Paradigms

9m 53s

Sampling Methods

19m 43s

Underfitting and Overfitting in Models

6m 7s

Variance and Bias

9m 49s

Assignment of Machine Learning Paradigms

3m 24s

Quiz : Machine Learning Paradigms

10m

Overview of Classification

5m 22s

Logistic Regression

4m 54s

Logistic Regression - Practical

32m 40s

KNN

7m 21s

KNN - Practical

35m

Decision Trees for Classification

6m 17s

Decision Trees Practical - 1

22m 41s

Decision Trees Practical - 2

20m 42s

Random Forest

8m 37s

Support Vector Machine Classifier

5m 44s

Support Vector Machine Classifier - Practical 1

16m 59s

Support Vector Machine Classifier - Practical 2

11m 18s

Naive Bayes Classifier

5m 25s

Naive Bayes Classifier - Practical 1

15m 21s

Naive Bayes Classifier - Practical 2

11m 7s

Evaluating Classification Models Performance

7m 31s

Evaluating Classification Models Performance - Practical

33m 2s

Assignment of Supervised Learning Algorithms

1m 12s

Quiz : Supervised Learning Algorithms

10m

Simple Linear Regression

8m 57s

Simple Linear Regression - Practical

12m 34s

Multi-Linear Regression

Support Vector Regressor

10m 47s

Support Vector Regressor - Practical

6m 47s

Decision Tree Regressor

4m 52s

Decision Tree Regressor - Practical

5m 2s

Regressor Model Selection

9m 7s

Evaluating Regression Model Performance

8m 45s

Evaluating Regression Model Performance - Practical

11m 34s

Assignment of Regression Algoritms

1m 12s

Quiz : Regression Algorithms

10m

Advance Trends in Machine Learning

10m 13s

Course Summary

3m 51s

Interview Questions Part 1

19m 22s

Interview Questions Part 2

12m 23s

Interview Questions Part 3

15m 20s

Career Guidelines

1m 33s

Decision Tree Regressor - Practical

Supervised Learning in Machine Learning Regression Algoritms

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Discussion

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5_4_Decision_Tree_Regressor

Course Content

Introduction to Machine Learning

1m 5s

Environment Setup part 1

3m 26s

Environment Setup part 2

4m 43s

Environment Setup part 3

2m 33s

Data Wrangling

12m 26s

Importing Libraries and Dataset

11m 19s

Handling Missing Data

11m 51s

Handling Missing Data - Practical

17m 3s

Encoding Categorical Data

6m 34s

Encoding Catergorical Data - Practical

8m 3s

Splitting Dataset

3m 38s

Splitting Dataset - Practical

9m 36s

Normalizing the Data - Part 1

6m 18s

Normalizing the Data - Part2

6m 58s

Finding Machine Learning Datasets

9m 17s

Exploratory Data Analysis

Plotting Graphs - Part 1

19m 12s

Plotting Graphs - Part 2

23m 44s

Distribution Models - Part 1

33m 54s

Distribution Models - Part 2

13m 56s

Assignment of Data Preprocessing for Machine Learning

1m 39s

Machine Learning Paradigms

9m 53s

Sampling Methods

19m 43s

Underfitting and Overfitting in Models

6m 7s

Variance and Bias

9m 49s

Assignment of Machine Learning Paradigms

3m 24s

Overview of Classification

5m 22s

Logistic Regression

4m 54s

Logistic Regression - Practical

32m 40s

KNN

7m 21s

KNN - Practical

35m

Decision Trees for Classification

6m 17s

Decision Trees Practical - 1

22m 41s

Decision Trees Practical - 2

20m 42s

Random Forest

8m 37s

Support Vector Machine Classifier

5m 44s

Support Vector Machine Classifier - Practical 1

16m 59s

Support Vector Machine Classifier - Practical 2

11m 18s

Naive Bayes Classifier

5m 25s

Naive Bayes Classifier - Practical 1

15m 21s

Naive Bayes Classifier - Practical 2

11m 7s

Evaluating Classification Models Performance

7m 31s

Evaluating Classification Models Performance - Practical

33m 2s

Assignment of Supervised Learning Algorithms

1m 12s

Simple Linear Regression

8m 57s

Simple Linear Regression - Practical

12m 34s

Multi-Linear Regression

Support Vector Regressor

10m 47s

Support Vector Regressor - Practical

6m 47s

Decision Tree Regressor

4m 52s

Decision Tree Regressor - Practical

5m 2s

Regressor Model Selection

9m 7s

Evaluating Regression Model Performance

8m 45s

Evaluating Regression Model Performance - Practical

11m 34s

Assignment of Regression Algoritms

1m 12s

Advance Trends in Machine Learning

10m 13s

Course Summary

3m 51s

Interview Questions Part 1

19m 22s

Interview Questions Part 2

12m 23s

Interview Questions Part 3

15m 20s

Career Guidelines

1m 33s

FAQs

Decision tree uses a tree structure to develop regression or classification models. It incrementally divides a dataset into smaller and smaller sections while also developing an associated decision tree. The end output is a tree with decision and leaf nodes.

A regression tree is constructed using binary recursive partitioning, which is an iterative procedure that divides the data into partitions or branches and then divides each partition into smaller groups as the approach progresses up each branch.

The major distinction between classification and regression decision trees is that classification decision trees are formed using unordered values and dependent variables, whereas regression decision trees are built with ordered values and dependent variables. The regression decision trees accept both ordered and continuous values.

Simple to comprehend and interpret.
Little data preparation is required.
The cost of using the tree (predicting data) is proportional to the quantity of data points needed to train the tree.
Capable of dealing with both numerical and category data.
Capable of dealing with multi-output difficulties.