In the realm of Data Science, machine learning algorithms play a key role in building predictive models. One such algorithm that is widely used due to its versatility and simplicity is Random Forest. Random Forest is a supervised learning algorithm, loved by many for its simplicity and ability to build robust models.
Random Forest is a boosting ensemble model; it creates a forest with a number of decision trees. In an ordinary decision tree algorithm, an optimal split can be made. However, this is not the case with Random Forest. Here, every node is split sub-optimally within this algorithm. This happens through choosing the best split point in the subset of features randomly chosen at every node. This introduces randomness into the model and hence decreases the variance of the model.
Now, let's use Random Forest for prediction using Python's Scikit-Learn library.
# Importing necessary libraries from sklearn.model_selection import train_test_split from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier from sklearn import metrics iris = load_iris() # Loading the iris dataset X = iris.data # Features y = iris.target # Target variable # Splitting the dataset into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3) # Create a Random Forest Classifier clf=RandomForestClassifier(n_estimators=100) # Training the model using training sets y_pred=clf.predict(X_test) clf.fit(X_train, y_train) y_pred=clf.predict(X_test) # Model Accuracy, how often is the classifier correct? print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
In the code snippet above, we first load the iris dataset, which is a multi-class classification problem. Then we split the dataset into training and testing sets. Afterward, we create a Random Forest Classifier and train the model using the training sets. In the end, we evaluate the model by computing the accuracy score.
Random Forest, by virtue of being a collection of decision trees, has proven to be a powerful algorithm in machine learning. It has both classification and regression capabilities, making it versatile in handling different types of data. The added benefit of easy usability in Python makes it a common choice among Data Scientists.
That's a brief explanation about the Random Forest algorithm in Data Science and how to implement it in Python. In the next blog, we'll tackle another random topic from the fascinating world of Data Science.