In this article, we will look at a Random Forest Classifier. The Random Forest model improves the tree model by training multiple tree models and select the best. This helps because a single tree model usually overfits. In this article, we will learn how to build a Random Forest Classifier using Sklearn.
To create a RandomForestClassifier, we use the
RandomForestClassifier class from the
ensemble module. We create a instance of
RandomForestClassifier then pass our data to the
fit method as we usually do when building models.
from sklearn.ensemble import RandomForestClassifier from sklearn import datasets iris = datasets.load_iris() features = iris.data target = iris.target randomforest = RandomForestClassifier() model = randomforest.fit(features, target) print(model.score())