Python Machine Learning Simple Random Forest Project
In this project, we will use the popular Python library, sklearn, to build a simple random forest classifier. Random forest is a versatile machine learning algorithm that can be used for both regression and classification tasks.
Authentication Code
First, we need to import the necessary libraries and authenticate to the sklearn library. Here is the code snippet for the authentication:
import sklearn
sklearn.__version__
Plotting the Decision Tree
Next, we will train our random forest model and plot one of the decision trees using the plot_tree function. This will help us visualize how the model is making decisions.
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.tree import plot_tree
X, y = make_classification(n_samples=1000, n_features=4,
n_informative=2, n_redundant=0,
random_state=0, shuffle=False)
clf = RandomForestClassifier(max_depth=2, random_state=0)
clf.fit(X, y)
plt.figure(figsize=(10, 10))
plot_tree(clf.estimators_[0], filled=True)
Random Forest Classifier
Finally, we will build our random forest classifier and evaluate its performance using cross-validation. Random forest is known for its high accuracy and ability to handle noisy data.
from sklearn.model_selection import cross_val_score
scores = cross_val_score(clf, X, y, cv=5)
print("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
This project showcases the power and simplicity of using random forest in Python for machine learning tasks. With sklearn, building and evaluating complex models like random forest is made easy and accessible to all levels of data scientists and developers.
Thank you for this. Just got my sub, keep it up!! 👍