Random Forest Done

Part 2 - Regression/Section 9 - Random Forest Regression/random_forest.py

@@ -8,12 +8,13 @@
 y = dataset.iloc[:, 2].values
 
 
-from sklearn.tree import DecisionTreeRegressor
-regressor = DecisionTreeRegressor(random_state=0)
+from sklearn.ensemble import RandomForestRegressor
+regressor = RandomForestRegressor(n_estimators=300, random_state=0)
 regressor.fit(X,y)
 
 #Predicting the results
 y_pred = regressor.predict(6.5)
+print(y_pred)
 
 #Visualizing the predictions
 X_grid = np.arange(min(X),max(X), 0.01)

README.md

@@ -8,4 +8,5 @@ Ensemble Learning: Take multiple algorithms or an algo multiple times to make a
 3) Choose the number Ntree of tree you want to build and repeat steps 1 & 2
 4) For a new data point, make each one of your Ntree trees predict the value of Y to for the data point in question, and assign the new data point the average across all of the predicted Y values
 
-Random Forest is a team of Decision Trees
+Random Forest is a team of Decision Trees.
+#### Note: The number of steps does not depend on the number of tress. The average of decision of each tree is noted.