Complete Machine Learning Course in English > Regression Algorithms

Decision Tree Regressor - Practical 1

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Overview

Hello,

I am (name) from LearnVern.

You are welcome to the Machine Learning Course, and this tutorial is in continuation of our previous session.

So, let's see ahead.

Now, we are going to see about the Decision Tree Regressor.

During the time of classification also, we have performed classification with the help of decision tree.

Now, we will try and perform regression, so in continuous output we will perform regression and see.

So, let's begin.

So, our first step is as usual we will import the libraries,

From sklearn dot datasets import load, this time we will choose a new dataset, which is of diabetes, so load diabetes, before that I will connect this as it is not giving me suggestions, because through suggestions it becomes easy as we get short cuts.

So, diabetes..

After this, the next thing that I want is from sklearn dot model selection import cross val score, so what will this cross val score do? This will basically help me in finding out the score.

Finally, the algorithm that I am going to try.

From sklearn dot tree import decision tree regressor.

So, these are all my libraries.

Now, I want a dataset, which I will take from load diabetes, so we know that, x comma y is equal to l o a d underscore diabetes, and this we have to write return xy and here we have to make it as true, True.

So, we got our x and y also.

So, we have our x and y now.

So, let's move ahead,

So, this is our output, and that x was our input.

Now, we will make one model for this, so D T R E G R regressor DT regressor is equal to decision tree regressor… and if you wish to pass any parameters, then you can do that I am just passing random state,and initialising it as zero, that's it.

So, this is my D T R E G R and with dot fit I will pass this dataset for learning.

X and y, so I am letting it learn.

And after learning I will let it do prediction with D T E G R, dot p r e d predict, and here I will pass only x.

So this is the prediction.

Right!

Now, in y we have the actual predictions that are observed.

So, here you can see for 151, it has also given us 151, for 75 it has given 75, for 141 also it is giving 141.

So, it has given the output perfectly.

Now, we will take out its cross val score.

For that I will put a cross val score, and here I put our regressor, which is D T E G R, so I have put the regressor, along with that I will put x and y and cross validation is equal to I will pass 10 as the value.

So, let me run this, so it will run on 10 samples.

So, you can see this is the score, which is not that great at the time of cross validation, because here all the data is an overfitting I would say, you can see here up, that giving the exact same output, this is call as over fitting,

So its score is not that nice, and if we want to improve its score, then ahead we will see in the performance metrics session, seeing that you can improve its score or improve the prediction.

So, this is the way the decision tree regressor works.

So friends let's conclude here for today. We will Stop this session here.

And it's upcoming parts we will cover in the next session.

Till then keep learning and remain motivated.

Thank you.

If you have any queries or comments, click the discussion button below the video and post there. This way, you will be able to connect to fellow learners and discuss the course. Also, Our Team will try to solve your query.

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Reviews

A
Aryan Ambat
5

Yes

Z
zeyana Fathima
5

thanks for giving this wonderful course in a understandable way please provide the details from where can i get the datasets

L
Losika Nicholas
5

were can i get the dataset

K
Kumar Madduru
5

Thanks for giving this course

D
Dinesh Kumar
4

Your screen is very blur and it doesn't has clarity even in 720P.Please make sure that will not happen again.

D
DOGALA UDAYKUMAR
5

bettor

N
Naresh Kulunge
4

good learning but the content titles are jumbled up, like first title of this module is decision tree dichotomiser which is practical part ahead of theory part. Same with the SVM practical 1 title has

E
Eswar Veeranki
5

good

I
Isakki Alias Devi P
5

Wonderful course

S
sushma Yadla
5

yes, i am happy to learning for machine learning in LearnVern.it i s easily understanding for Beginners.

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Course Content

  1. Getting Started with Machine Learning
    4m 4s
  2. How to use LearnVern
    3m 8s

  1. Introduction to Machine Learning
    16m 19s
  2. Environment Setup Part 1
    3m 21s
  3. Environment Setup Part 2
    4m 52s
  4. Environment Setup Part 3
    2m 35s

  1. Data Wrangling
    12m 36s
  2. Importing Libraries and Dataset
    11m 35s
  3. Handling Missing Data
    12m 13s
  4. Handling Missing Data - Practical
    19m 46s
  5. Encoding Categorical Data
    7m 3s
  6. Encoding Catergorical Data - Practical
    8m 6s
  7. Splitting Dataset
    3m 21s
  8. Splitting Dataset - Practical
    9m 43s
  9. Normalizing the Data - Part 1
    7m 29s
  10. Normalizing the Data - Part 2
    7m 30s
  11. Finding Machine Learning Datasets
    8m 56s
  12. Exploratory Data Analysis
    9m 49s
  13. Plotting Graphs - Part 1
    17m 51s
  14. Plotting Graphs - Part 2
    21m 44s
  15. Distribution Models - Part 1
    29m 48s
  16. Distribution Models - Part 2
    10m 23s
  17. Assignment : Data Preprocessing for Machine Learning
    1m 54s

  1. Machine Learning Paradigms
    10m 37s
  2. Assignment : Machine Learning Paradigms
    3m 22s

  1. Decision Tree Iterative Dichotomiser 3
    20m 2s
  2. Random Forest
    5m 25s
  3. Support Vector Machine Classifier
    6m 22s
  4. Support Vector Machine Classifier - Practical 1
    16m 16s
  5. Support Vector Machine Classifier - Practical 2
    10m 25s
  6. Naive Bayes Classifier
    6m 36s
  7. Naive Bayes Classifier - Practical 1
    14m 34s
  8. Naive Bayes Classifier - Practical 2
    9m 42s
  9. Evaluating Classification Models Performance
    9m 8s
  10. Evaluating Classification Models Performance - Practical
    29m 33s
  11. Overview of Classification
    6m 16s
  12. Logistic Regression
    5m 35s
  13. Logistic Regression - Practical - 1
    30m 22s
  14. Logistic Regression - Practical - 2
    13m 28s
  15. KNN
    7m 34s
  16. KNN Practical - 1
    29m 39s
  17. KNN - Practical 2
    9m 53s
  18. Decision Trees for Classification
    6m 1s
  19. Decision Trees for Classification - Practical 1
    23m 27s
  20. Decision Trees for Classification - Practical 2
    18m 39s
  21. Assignment : Supervised Learning Algorithms
    1m 34s

  1. Simple Linear Regression
    10m 4s
  2. Simple Linear Regression - Practical
    11m 46s
  3. Salary Prediction using Linear Regression
    19m 25s
  4. Multi-Linear Regression
    2m 56s
  5. Startup Prediction using Multiple Regression
    20m 34s
  6. Support Vector Regressor
    11m 15s
  7. Support Vector Regressor - Practical 1
    7m 28s
  8. Support Vector Regressor - Practical 2
    14m 44s
  9. Decision Tree Regressor
    5m 31s
  10. Decision Tree Regressor - Practical 1
    5m 33s
  11. Decision Tree Regressor - Practical 2
    16m 31s
  12. Regressor Model Selection
    11m 8s
  13. Evaluating Regression Model Performance
    10m 45s
  14. Evaluating Regression Model Performance - Practical
    12m 11s
  15. Assignment : Regression Algorithms
    1m 5s

  1. Distance Metrics
    6m 1s
  2. K-Means Clustering
    11m 40s
  3. K-Means Clustering - Practical
    9m 32s
  4. Mall Customers Prediction using K Means Clustering
    16m 42s
  5. Hierarchical Clustering - Agglomerative , Divisive
    5m 25s
  6. Agglomerative Clustering - Practical
    4m 37s
  7. Divisive Clustering - Practical
    13m 43s
  8. DBscan Spatial Clustering
    10m 53s
  9. Mall Customers Prediction using Hierarchical Clustering
    17m 48s
  10. Assignment : Unsupervised Learning Algorithms
    3m 11s

  1. Association Rule Learning - Apriori, FP Growth
    8m 17s
  2. Association Rule Learning - Apriori Practical
    7m 41s
  3. Market Basket Analysis using Apriori
    19m 30s
  4. FP Growth
    8m
  5. Market Basket Analysis using FP Growth
    12m 20s
  6. Assignment : Association Rule Mining
    56s

  1. Reinforcement Learning Theory - Multi Armed Bandits
    27m 30s
  2. Upper Confidence Bound - Practical
    10m 53s
  3. Thompson Sampling - Practical
    5m 34s
  4. Q Learning
    32m 24s
  5. Assignment : Reinforcement Learning
    1m 43s

  1. Overview of Dimensoionality Reduction
    4m 40s
  2. Princinpal Component Analysis
    39m 4s
  3. Principal Component Analysis - Practical
    6m 37s
  4. Linear Discriminant Analysis
    19m 9s
  5. Linear Discriminant Analysis - Practical
    6m 29s
  6. Assignment : Dimensionality Reduction
    52s

  1. Basics of Regularization and Optimization
    7m 58s
  2. Cross Validation
    20m 3s
  3. Hyperparameter Tuning
    20m 7s
  4. Sampling Methods
    19m 8s
  5. Underfitting and Overfitting in Models
    5m 33s
  6. Variance and Bias
    9m 33s
  7. Assignment : Regularization and Optimization
    1m 1s

  1. Advance Trends in Machine Learning
    12m 48s

  1. Introduction to Keras and Deep Learning
    9m 2s
  2. Practical Demonstration -Keras
    18m 35s

  1. Reinforcement Learning Project - Teach a Taxi Part 1
    18m 10s
  2. Reinforcement Learning Project - Teach a Taxi Part 2
    15m 11s
  3. Reinforcement Learning Project - Teach a Taxi Part 3
    14m 35s
  4. Reinforcement Learning Project - Teach a Taxi Part 4
    17m 23s
  5. Loan Prediction Project Part 1
    32m 16s
  6. Loan Prediction Project Part 2
    28m 42s

  1. Course Summary
    3m 53s

  1. Interview Questions Part 1
    18m 22s
  2. Interview Questions Part 2
    13m 23s
  3. Interview Questions Part 3
    16m 2s

  1. Career Guidelines
    6m 14s
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