Tutorial 43-Random Forest Classifier and Regressor

Summary

In this YouTube video, Krishna explores the concept of random forests, a machine learning technique that utilizes bagging with decision trees. He explains how random forests work by creating multiple decision trees using row and feature sampling, which helps in reducing variance and improving model accuracy. Krishna highlights the difference between using a single decision tree, which can lead to overfitting, and the ensemble approach of random forests that results in lower bias and variance. He also touches on the use of majority voting for classification and averaging/median for regression in random forests, emphasizing their effectiveness in various machine learning applications.

Takeaways

• 🌳 The video introduces Random Forests, a machine learning technique that uses an ensemble of decision trees.
• 🔄 Random Forest is a type of bagging technique, which involves creating multiple models to improve accuracy and control overfitting.
• 🌱 The base learner in a Random Forest is the decision tree, and multiple decision trees are used to form the forest.
• 🔢 The script explains how Random Forests handle both classification and regression problems, using majority voting for classification and averaging/median for regression.
• 🔄 The process involves random sampling with replacement for both rows and features, which helps in creating diverse decision trees.
• 📉 The video highlights that decision trees can suffer from high variance, but Random Forests mitigate this by combining multiple trees through majority voting.
• 🔑 The script emphasizes the importance of hyperparameters, particularly the number of decision trees, in tuning a Random Forest model.
• 💡 Random Forests are robust to changes in the dataset because of the random sampling of rows and features, leading to lower variance in predictions.
• 🏆 The video mentions that Random Forests are a favorite algorithm among developers and work well for most machine learning use cases.
• 📈 The video concludes with a call to action for viewers to subscribe, share, and engage with the content for more learning opportunities.

Q & A

What is the main topic discussed in Krishna's YouTube video?

The main topic discussed in Krishna's YouTube video is Random Forests, which is a bagging technique used in machine learning for both classification and regression tasks.

What is bagging and how does it relate to random forests?

Bagging, or Bootstrap Aggregating, is a technique where multiple models are built on different subsets of the original dataset and then aggregated to improve the stability and accuracy of the model. Random forests use this technique by building multiple decision trees on different subsets of the data and then aggregating their predictions.

How does row sampling with replacement work in the context of random forests?

Row sampling with replacement in random forests involves selecting a subset of rows from the dataset for training each decision tree. This process is repeated with replacement, allowing the same row to be selected more than once, which helps in creating diverse subsets for each tree.

What is feature sampling with replacement and why is it used in random forests?

Feature sampling with replacement is the process of selecting a subset of features from the dataset for training each decision tree. This is used in random forests to further diversify the training data for each tree, which helps in reducing the variance of the model.

Why are decision trees used as the base learner in random forests?

Decision trees are used as the base learner in random forests because they are easy to interpret, handle non-linear relationships well, and can be easily combined using majority voting for classification or averaging for regression.

What is the role of D and D- in the context of random forest training?

In the context of random forest training, D represents the total number of records in the dataset, and D- represents the number of records in the sample used to train each decision tree. D- is always less than D because only a subset of the records is used for training each tree.

How does random forest handle the high variance problem associated with individual decision trees?

Random forests handle the high variance problem by using multiple decision trees and aggregating their predictions through majority voting for classification or averaging for regression. This ensemble approach reduces the overall variance of the model.

What is the significance of majority voting in the context of random forest classifiers?

Majority voting in random forest classifiers is a method of aggregation where the final prediction is made based on the most common prediction among all the decision trees. This helps in reducing the impact of any single tree's prediction and improves the overall accuracy of the model.

How does random forest handle regression problems?

In regression problems, random forests handle the output by calculating the mean or median of the continuous values predicted by each decision tree. The choice between mean and median depends on the distribution of the output values.

Why are random forests popular among machine learning practitioners?

Random forests are popular among machine learning practitioners because they tend to perform well on a variety of datasets, are less prone to overfitting, and can handle both classification and regression tasks effectively. They also provide a good balance between bias and variance.

What is the importance of hyperparameters in tuning a random forest model?

Hyperparameters in random forests, such as the number of decision trees, are crucial for tuning the model's performance. The right balance of hyperparameters can lead to better generalization and improved accuracy on unseen data.

Outlines

00:00

🌲 Introduction to Random Forests and Bagging

In this paragraph, Krishna introduces the topic of random forests and explains that they are an extension of the bagging technique, which was discussed in a previous video. Random forests utilize decision trees, and Krishna walks through how a dataset is used in this model. The dataset is split into subsets through row and feature sampling, which is then fed to multiple decision trees. Krishna emphasizes that the process involves sampling with replacement, ensuring varied decision trees are trained on different portions of the data.

05:01

📊 Low Bias and High Variance in Decision Trees

This paragraph dives deeper into the concepts of low bias and high variance, especially when decision trees are grown to their full depth. Krishna explains that while training data performs well (low bias), decision trees can overfit and show high variance with new test data. To mitigate this, random forests use multiple decision trees and combine their outputs using a majority vote. By using this ensemble technique, random forests convert high variance into low variance, leading to better predictions and accuracy.

10:01

🎯 Accuracy and Robustness of Random Forests

Here, Krishna highlights the robustness of random forests, explaining that changing a portion of the data doesn't significantly impact the model's performance due to the distributed nature of row and feature sampling. He explains how random forests maintain low variance, ensuring consistent accuracy even when test data changes. The paragraph emphasizes how random forests, due to their design, handle machine learning tasks effectively, and this makes them a favorite algorithm for many developers.

🔧 Differences Between Classification and Regression in Random Forests

In this paragraph, Krishna explains the differences between using random forests for classification and regression. For classification tasks, the output is based on a majority vote, whereas for regression tasks, the average or median of the decision trees’ outputs is used. He also touches on hyperparameters, specifically how the number of decision trees can be optimized for performance. This paragraph rounds off the explanation of how random forests are used for both tasks and provides insights into how these models can be fine-tuned.

📢 Conclusion and Call to Action

Krishna concludes the video by encouraging viewers to subscribe to the channel and share the video with anyone who might benefit from it. He emphasizes that all the materials presented are free to share and expresses his gratitude to the viewers. Krishna wraps up by wishing everyone a great day and promising more informative content in future videos.

Keywords

💡Random Forest

A random forest is an ensemble learning technique that combines multiple decision trees to improve accuracy. In the video, it is described as a bagging technique where multiple decision trees work together by row and feature sampling to make predictions, either for classification or regression problems.

💡Bagging

Bagging, or Bootstrap Aggregating, is a method that improves machine learning model performance by training multiple models on different samples of data. In the video, bagging is highlighted as a key part of how random forests function, using row and feature sampling with replacement to generate diverse decision trees.

💡Decision Tree

A decision tree is a model used for classification or regression that splits data based on specific criteria. In a random forest, each decision tree learns from a different subset of data and focuses on particular rows and features, which reduces error through the collective voting mechanism of multiple trees.

💡Row Sampling

Row sampling refers to the process of selecting a subset of data rows (with replacement) for training a model. In the context of random forests, row sampling is used to create diverse datasets for each decision tree, which helps avoid overfitting and makes the ensemble more robust.

💡Feature Sampling

Feature sampling involves selecting a subset of data features or columns to train each decision tree. In the random forest method, this technique helps ensure that each decision tree specializes in different patterns or aspects of the data, contributing to the overall model accuracy by reducing correlation among trees.

💡Overfitting

Overfitting occurs when a model learns the noise or specific details of the training data too well, leading to poor generalization on new data. In the video, decision trees that are trained to their complete depth are prone to overfitting, which random forests mitigate by combining multiple trees through bagging.

💡High Variance

High variance indicates that a model's predictions vary significantly when exposed to different datasets, making it sensitive to noise. In random forests, individual decision trees may have high variance, but the collective averaging of multiple trees reduces the overall variance and improves stability.

💡Low Bias

Low bias means a model makes accurate predictions on the training data but may struggle with new or unseen data. In the video, decision trees trained deeply have low bias but high variance, which random forests balance by using multiple trees trained on different subsets of data.

💡Majority Vote

Majority vote is the mechanism used in random forest classifiers to aggregate the predictions from multiple decision trees. In the video, it's explained that if most of the decision trees classify a test instance into one class, that becomes the final prediction, which stabilizes the output and improves accuracy.

💡Regression

Regression is a type of prediction task where the output is a continuous value rather than a category. In the video, random forests used for regression take the average or median of the output from multiple decision trees, instead of using a majority vote, to generate the final prediction.

Highlights

Introduction to Random Forests as an extension of bagging techniques.

Random Forests use multiple decision trees to enhance model accuracy and reduce overfitting.

Explanation of row sampling and feature sampling with replacement in Random Forests.

The process of training each decision tree using different samples of rows and columns.

Importance of combining multiple decision trees for reducing high variance in the model.

Decision trees, when used individually, tend to have low bias and high variance.

The role of majority voting in Random Forest classifiers for binary classification problems.

For regression problems, Random Forests use the mean or median of decision tree outputs instead of majority voting.

Random Forests can handle changes in data more robustly due to the row and feature sampling approach.

Random Forests convert high variance into low variance by aggregating results from multiple decision trees.

Decision trees trained to their complete depth may lead to overfitting in some scenarios.

Row and feature sampling allow decision trees to specialize in specific parts of the dataset.

Random Forests generally provide high accuracy across various machine learning tasks.

The importance of tuning hyperparameters, such as the number of decision trees in Random Forest models.

The concept that Random Forests are highly effective for both classification and regression tasks.

Transcripts

hello all my name is Krishna and welcome

to my youtube channel today we are going

to discuss about random forests now in

my previous video I have already put up

a video on bagging and I told you that

one of the technique that is basically

mostly used is something called as

random forest so random forest

classifier or a regresar is basically a

bagging technique and we are going to

discuss both of them in this particular

session so let me just consider and let

me just show you some example suppose I

have a data set now how does random

forest basically work suppose this is my

data set D now I told you that in

bagging we basically have many based

learners based learning models so this

suppose this is my m1 model this is my

m2 model this is my m3 model and many

more models like this okay so suppose

this is my MN model now when we are

designing this particular model in the

random forest this model is basically

called as decision trees we are going to

use decision trees in this model and as

I had explained in the bagging technique

suppose in this particular data set we

have the D records okay D number of

records and M number of columns

suppose I have that many columns n

number of columns so what we do is that

from this particular data set we will be

picking up some sample of rows and some

sample of features okay

so initially I will pick up some sample

of rows I will say it as row sampling

row sampling with replacement I just say

what is that particular placement term

means so I'm going to take some rows

from this particular data set and

similarly I am going to pick up some

columns okay or I can also write this as

feature sample so FS okay feature sample

replacement now that is how backing

words works right we will be taking some

amount of rows given to our decision

tree one so this is really a decision

tree one decision tree two three four

and okay so all this decision tree

suppose I say that this particular data

set is basically D - I always remember

when I say D - D - is always less than D

because the number of Records from here

I'm just taking a sample of Records and

suppose if I consider that I have taken

and suppose small D - rows and n columns

right n number of features so always

remember this M will always be greater

than n

and this D - this capital D will always

be greater than D - or small D I'll say

it as small D because the total number

of Records I have written as D okay so

always remember that guys I am going to

take some number of rows some number of

features give it to my decision tree one

this decision tree one will get trained

on this particular data set now

similarly from a decision tree to what

I'll do is that again this row sampling

will happen with replacement now what

does with replacement mean is that oh

here suppose from this particular record

I have some of the records some of the

records it may come into this particular

scenario so when I am doing row sampling

with replacement not all the records

will get replayed repeated but instead

I'll be taking another sample of records

and give to our decision tree - so when

I'm doing again a row sampling + feature

sampling over here it may be it may

happen that some of the records may get

repeated here some of the features and

get repeated here but we are at least

changing many records again we are doing

this row sampling okay row sampling and

feature sampling so suppose in this

particular case I had given feature one

two three four five suppose in this

particular case I will give other

features like feature 1 3 4 5 6 7 like

that and similarly that row sampling

also happens in the similar way now

after doing this row sampling and future

sampling I will give this particular

records to my decision tree - this will

get trained on this particular data

similarly for every decision tree this

thing is going to happen where you are

going to perform row sampling and

feature sampling ok row sampling and

feature sampling now this decision tree

gets trained on this particular data ok

and now it will be able to give them

accuracy or it will be able to give the

prediction now the next thing is that

whenever I get my test data whenever I

get my test data suppose I am giving one

record of the test data into this

particular decision tree one suppose

decision tree one suppose I am

considering a binary classification

problem decision tree once gives me one

this also gives me one this gives me 0

and suppose this gives me 1 okay now

when we see over here finally we know

that this is my bootstrap and now

according to the bagging finally after

aggregate it right so for aggregating I

am going to use the majority would now

when I use the majority would I know

that the max

of models that has basically st. output

is like one so away I can see one two

three models is basically saying it as

one so finally my output is basically

one now this is how a decision random

forest basically works the based learner

is decision tree now you need to

understand one more thing in this what

is happening if when we are using many

decision trees in this particular random

forest because you should know that

decision tree whenever I use decision

tree it has two properties suppose if I

am creating a decision tree to its

complete depth so when I do that it

basically has low bias and high variance

I'm going to explain about what is low

bias and high variance just let me write

it down for Stauffer so low bias

basically says that if I am creating my

decision tree to its complete depth then

what will happen is that it will get

properly trained for our training data

set okay so the training error will be

very very less high violence high

variance basically says that now

whenever we get our new test data those

decision tree they are prone to give

larger amount of errors so that is

basically called as high variance okay

so in short whenever we are creating the

decision tree to its complete depth it

leads to something called as overfitting

okay so now what is happening in random

forests in random forests I am basically

using multiple decision tree right and

we know that each and every decision

tree will be having high variance right

but when we combine all the decision

tree with respect to this majority vote

what will happen is that this high

variance will get converted into low

variance because now when we are using

row sampling and feature sampling and

giving the records to the decision tree

the decision tree tends to become an

expert with respect to this specific

rows or the data set that they have okay

since we are giving different different

records to each and every decision tree

they become an expert with respect to

those records they get trained on that

particular data specifically and in

order to convert this high variance to

low variance we are basically taking the

majority vote okay we are not just

depending

on one decision tree output so because

of that this high variance will get

converted into low variance when we are

combining multiple decision til now one

more advantage you need to understand

suppose i have thousand records over

here now in this thousand records okay

suppose I just changed let me just

change two hundred records will this

change of the data impact this random

forest now understand guys we are doing

random sampling sorry rose sampling and

feature sampling for each and every

decision tree now if I just change to

one hundred records now this two hundred

records will be properly splitted

between this data decision tree so when

it is actually splitted then what will

happen is that some of the number of

roles or some of the number of records

will go to decision tree one then

decision tree two then three then four

so this data change will also not make

that much impact to a decision tree with

respect to the accuracy or with respect

to the output so that is why this high

variance

even though whenever we change our data

whenever we change our test data we will

be getting a low variance error or our

error rate will be very very low our

accuracy will be very very good since we

are taking the majority of what we are

doing row sampling and feature sampling

giving to the decision trees now this is

the most important property of random

forests so random forest actually works

very well with respect to most for the

machine learning use cases that you are

basically trying to do and I've seen in

most of the companies developer have

made the favorite favorite algorithm as

random forest let it be classifier

aggressor one more point I missed out is

that suppose if this is not a binary

classification it is a regression

problem what will happen now this

particular decision tree suppose it

gives me a continuous value this also

gives me a continuous value this also

gives me a continuous value for that

what we do is that in the regression

problem we either take the mean of all

this particular output or the median of

that particular output it depends on the

distribution of the output how the

decision tree is basically given so

usually the main random forest that

works with respect to a scale on it

tries to find out the average of this

particular output from all the decision

tools and that is much a simple it

you need to understand if I just use

single decision tree it will have low

bias and high variance if I want to

convert this high by high variance into

low variance I have to basically use

multiple decision tree apart from that I

also have to use row sampling and

feature sampling so that I will be able

to convert that into low variance that

basically is our accuracy for the new

data or the test data will be very very

good so this was all about random

forests and I have explained you both

about classifier and regressor only the

difference between classifier and

regression is that classify uses

majority wood I'll just write it down

majority wood whereas in the case of

regression it will actually find out the

mean or the median of the particular

output of all the decision trees now the

hyper parameter that you have to

basically work on in that how many

decision trees you have to actually use

for the random forest okay how many

decision trees you have to basically use

so by with the help of hyper parameter

you'll be able to work that out okay so

this was all about the video of random

forest classifier and regression I hope

you like this particular video please

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I'll see you all in the next video have

a great day thank you one and all