Raqamli texnologiyalarning Yangi O‘zbekiston rivojiga ta’siri

Random Forest Regression:
RFR
is a supervised learning algorithm that uses 
ensemble 
learning
method for regression. Ensemble learning method is a technique that combines predictions 
from multiple machine learning algorithms to make a more accurate prediction than a single 
model.
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In random forest regression, the ensemble is composed of multiple decision trees. Each 
decision tree is constructed using a subset of the training data and a random selection of features. 
The random selection of features helps to introduce diversity among the trees and reduces 
overfitting.
The key steps in building a random forest regression model are as follows: 
-
Data Preparation: Prepare the training data with a set of independent variables and their 
corresponding dependent variable. 
-
Random Sampling: Randomly select subsets of the training data (with replacement) to create 
multiple subsets, known as bootstrap samples. Each bootstrap sample is used to train an individual 
decision tree. 
-
Decision Tree Construction: For each bootstrap sample, construct a decision tree by 
recursively splitting the data based on the selected features. The splits are determined using a 
criterion such as the Gini index or information gain. 
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https://levelup.gitconnected.com/random-forest-regression-209c0f354c84 

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RAQAMLI TEXNOLOGIYALARNING 
YANGI 
O‘ZBEKISTON
 RIVOJIGA 
TA’SIRI
 
Xalqaro ilmiy-amaliy konferensiyasi
 
-
Ensemble Generation
:
Generate an ensemble by combining the predictions from all the 
individual decision trees. In random forest regression, the predictions are typically averaged or 
aggregated to obtain the final prediction. 
-
Prediction: Use the trained random forest model to make predictions on new data by 
aggregating the predictions from all the individual decision trees. 
Random forest regression is commonly used for various applications, including prediction, 
forecasting, and feature selection. However, it is important to tune the hyperparameters of the 
random forest model, such as the number of trees, maximum depth, and minimum samples per leaf, 
to achieve optimal performance.