Every decision tree has high variance, but when we combine all of them together in parallel then the resultant variance is low as each decision tree gets perfectly trained on that particular sample data, and hence the output doesn’t depend on one decision tree but on multiple decision trees. In the case of a classification problem, the final output is taken by using the majority voting classifier. In the case of a regression problem, the final output is the mean of all the outputs. This part is called Aggregation.
Random Forest Regression Model Working
What is Random Forest Regression?
Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as bagging. The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees.
Random Forest has multiple decision trees as base learning models. We randomly perform row sampling and feature sampling from the dataset forming sample datasets for every model. This part is called Bootstrap.
We need to approach the Random Forest regression technique like any other machine learning technique.
- Design a specific question or data and get the source to determine the required data.
- Make sure the data is in an accessible format else convert it to the required format.
- Specify all noticeable anomalies and missing data points that may be required to achieve the required data.
- Create a machine-learning model.
- Set the baseline model that you want to achieve
- Train the data machine learning model.
- Provide an insight into the model with test data
- Now compare the performance metrics of both the test data and the predicted data from the model.
- If it doesn’t satisfy your expectations, you can try improving your model accordingly or dating your data, or using another data modeling technique.
- At this stage, you interpret the data you have gained and report accordingly.
You will be using a similar sample technique in the below example. Below is a step-by-step sample implementation of Random Forest Regression, on the dataset that can be downloaded here- https://bit.ly/417n3N5
Import Libraries and Datasets
Python libraries make it very easy for us to handle the data and perform typical and complex tasks with a single line of code.
- Pandas – This library helps to load the data frame in a 2D array format and has multiple functions to perform analysis tasks in one go.
- Numpy – Numpy arrays are very fast and can perform large computations in a very short time.
- Matplotlib/Seaborn – This library is used to draw visualizations.
- Sklearn – This module contains multiple libraries having pre-implemented functions to perform tasks from data preprocessing to model development and evaluation.
- RandomForestRegressor – This is the regression model that is based upon the Random Forest model or the ensemble learning that we will be using in this article using the sklearn library.
Python3
# Importing the librariesimport numpy as npimport matplotlib.pyplot as pltimport pandas as pd |
Now let’s load the dataset in the panda’s data frame. For better data handling and leveraging the handy functions to perform complex tasks in one go.
Python3
data = pd.read_csv('Salaries.csv')print(data) |
Output:
Position Level Salary 0 Business Analyst 1 45000 1 Junior Consultant 2 50000 2 Senior Consultant 3 60000 3 Manager 4 80000 4 Country Manager 5 110000 5 Region Manager 6 150000 6 Partner 7 200000 7 Senior Partner 8 300000 8 C-level 9 500000 9 CEO 10 1000000
Select all rows and column 1 from the dataset to x and all rows and column 2 as y.
Python3
x = df.iloc[:, : -1]y = df.iloc[:, -1:] |
The function enables us to select a particular cell of the dataset, that is, it helps us select a value that belongs to a particular row or column from a set of values of a data frame or dataset.
Random Forest Regressor Model
Python3
# Fitting Random Forest Regression to the dataset# import the regressorfrom sklearn.ensemble import RandomForestRegressor# create regressor objectregressor = RandomForestRegressor(n_estimators=100, random_state=0)# fit the regressor with x and y dataregressor.fit(x, y) |
Output:
Predicting a new result.
Python3
# test the output by changing valuesY_pred = regressor.predict(np.array([6.5]).reshape(1, 1)) |
Now let’s visualize the results obtained by using the RandomForest Regression model on our salaries dataset.
Python3
# Visualising the Random Forest Regression results# arrange for creating a range of values# from min value of x to max# value of x with a difference of 0.01# between two consecutive valuesX_grid = np.arrange(min(x), max(x), 0.01)# reshape for reshaping the data # into a len(X_grid)*1 array,# i.e. to make a column out of the X_grid valueX_grid = X_grid.reshape((len(X_grid), 1))# Scatter plot for original dataplt.scatter(x, y, color='blue')# plot predicted dataplt.plot(X_grid, regressor.predict(X_grid), color='green')plt.title('Random Forest Regression')plt.xlabel('Position level')plt.ylabel('Salary')plt.show() |
Output:
Out of Bag Score in RandomForest
Bag score or OOB score is the type of validation technique that is mainly used in bagging algorithms to validate the bagging algorithm. Here a small part of the validation data is taken from the mainstream of the data and the predictions on the particular validation data are done and compared with the other results.
The main advantage that the OOB score offers is that here the validation data is not seen by the bagging algorithm and that is why the results on the OOB score are the true results that indicated the actual performance of the bagging algorithm.
To get the OOB score of the particular Random Forest algorithm, one needs to set the value “True” for the OOB_Score parameter in the algorithm.
Python3
from sklearn.trees import RandomForestClassifierRandomeForest = RandomForestClassifier(oob_score=True)RandomForest.fit(X_train,y_train)print(RandomForest.oob_score_) |
Advantages Random Forest Regression
- It is easy to use and less sensitive to the training data compared to the decision tree.
- It is more accurate than the decision tree algorithm.
- It is effective in handling large datasets that have many attributes.
- It can handle missing data, outliers, and noisy features.
Disadvantages Random Forest Regression
- The model can also be difficult to interpret.
- This algorithm may require some domain expertise to choose the appropriate parameters like the number of decision trees, the maximum depth of each tree, and the number of features to consider at each split.
- It is computationally expensive, especially for large datasets.
- It may suffer from overfitting if the model is too complex or the number of decision trees is too high.
