The random forest model in Python is a powerful and versatile algorithm that is used for both regression and classification tasks. The “forest” in random forest comes from the fact that a bunch of decision trees are clubbed together to formulate the model, hence the name random forest. It comes under the category of ensemble learning models where a collection of individual predictions makes an overall stronger prediction.
There are many advantages of using the RandomForest model for classification and regression tasks such as efficiency since multiple decision trees are used, prevention in overfitting of data which makes it more robust, and it is also widely used for feature elimination and selection according to specific needs.
Despite its effectiveness, sometimes it becomes difficult to achieve optimal accuracy when dealing with complex and large datasets. Incorporating linear regression techniques can help us in increasing the accuracy of RandomForest models in Python. In this article, we will look at how random forest works and how we can determine the accuracy of a RandomForest model in Python using linear regression. Let’s get into it!
Advantages and Disadvantages of Random Forest Algorithm
The random forest model works by creating an “ensemble” of decision trees, each built from a random subset of training data and features. This ensemble approach has several key benefits:
- More Robust Predictions: Each decision tree makes an independent prediction. These predictions are aggregated through a “majority votes” system to produce the overall random forest prediction. Using multiple trees protects against overfitting and improves generalizability.
- Randomness Reduces Correlation: By building trees using different random subsets of features and data, the decision trees are decorrelated from each other. This results in greater diversity among the trees, and more robust predictions.
- Quantifying Prediction Uncertainty: The number of trees predicting a particular outcome can provide insight into prediction certainty. Outcomes predicted by a larger proportion of trees are less uncertain.
- Feature Importance Identification: Features that are commonly used by trees to make correct predictions can be deemed more “important”. This is useful for feature engineering and selection.
However, random forest models also come with some limitations:
- Interpretability: Interpreting the reasoning inside multiple complex tree models can be difficult compared to simpler models.
- Overfitting Risk: While less susceptible, random forests can still overfit with too many trees. Careful cross-validation is required.
- Computationally Expensive: Running many decision trees on large datasets requires considerable processing resources. Performance needs to be monitored.
Using a linear regression model stacked upon this ensemble model can help increase the accuracy of the randomforest algorithm. If multiple base models are trained, along with gradient boosting techniques, it can combine the predictions of multiple weak models and make a stronger prediction.
Recommended: Logistic Regression in Predictive Analytics: A Comprehensive Guide.
Stacking a Linear Regression Model to Improve Accuracy in Python
In this section, we will be using the sklearn module which contains the framework of all of these models and then we can use it to get the accuracy of a stacked model consisting of a random forest model and a linear regression model. We will be using the iris dataset with 150 entries with 4 different features and 3 different classes of flowers. Each class has 50 entries.
#importing required functiond from sklearn
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import StackingRegressor
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import make_pipeline
from sklearn.datasets import load_iris
# Loading the Iris dataset
iris = load_iris()
X = iris.data # Features
y = iris.target # Target variable (species)
# Loading and preprocessing the dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
# Initializing base models
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
lr_model = LinearRegression()
# Creating the stacked model
stacked_model = StackingRegressor(estimators=[('rf', rf_model), ('lr', lr_model)])
# Training the stacked model
stacked_model.fit(X_train_scaled, y_train)
# Evaluating the stacked model using accuracy
accuracy = stacked_model.score(X_test_scaled, y_test)
print("Accuracy:", accuracy)
In the above code, we have loaded the iris dataset preprocessed the data, and split it into testing and training sets for the linear regression model (lr_model). We have also initialized the random forest model with 100 trees and a random state of 42. Then we stacked the two models in one stacked model and used the model on the iris dataset. Then we have found out the accuracy of the model is about 98% which is very good, as given below.
Accuracy: 0.9881224042888985
Suggested: Linear Regression from Scratch in Python.
Summary
To increase the accuracy of our randomforest model, we can integrate it with a linear regression model and can formulate stacked algorithms to improve performance and optimization. This integration enables our model to predict outcomes more accurately and to stand the test of time by being more robust and efficient. Experimentation with different integration techniques and ensemble models can help us hyper tune our features and parameters and make our models more useful in real life.