9/16/2023 0 Comments Random forest pros and cons![]() The objective of the algo is to predict whether X will like a movie not present in the training sample, based on certain attributes. The prediction process using random forests is very time-consuming in comparison with other algorithms.Chapter 6 of the the book goes in to showcasing a simple dataset that contains movies watched by X based on certain attributes. It is less intuitive in case when we have a large collection of decision trees. More computational resources are required to implement Random Forest algorithm. The following are the disadvantages of Random Forest algorithm −Ĭomplexity is the main disadvantage of Random forest algorithms.Ĭonstruction of Random forests are much harder and time-consuming than decision trees. It maintains good accuracy even after providing data without scaling. Scaling of data does not require in random forest algorithm. Random forests are very flexible and possess very high accuracy. ![]() Random forest has less variance then single decision tree. Random forests work well for a large range of data items than a single decision tree does. It overcomes the problem of overfitting by averaging or combining the results of different decision trees. ![]() The following are the advantages of Random Forest algorithm − Result1 = classification_report(y_test, y_pred) Result = confusion_matrix(y_test, y_pred) It can be done with the help of following script −įrom trics import classification_report, confusion_matrix, accuracy_score Next, train the model with the help of RandomForestClassifier class of sklearn as follows −įrom sklearn.ensemble import RandomForestClassifierĬlassifier = RandomForestClassifier(n_estimators=50)Īt last, we need to make prediction. X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30) The following code will split the dataset into 70% training data and 30% of testing data −įrom sklearn.model_selection import train_test_split Next, we will divide the data into train and test split. Now, we need to read dataset to pandas dataframe as follows −ĭataset = pd.read_csv(path, names=headernames)ĭata Preprocessing will be done with the help of following script lines − Next, we need to assign column names to the dataset as follows − Next, download the iris dataset from its weblink as follows − The following diagram will illustrate its working − Implementation in Pythonįirst, start with importing necessary Python packages − Step 4 − At last, select the most voted prediction result as the final prediction result. Step 3 − In this step, voting will be performed for every predicted result. Then it will get the prediction result from every decision tree. Step 2 − Next, this algorithm will construct a decision tree for every sample. Step 1 − First, start with the selection of random samples from a given dataset. We can understand the working of Random Forest algorithm with the help of following steps − It is an ensemble method which is better than a single decision tree because it reduces the over-fitting by averaging the result. Similarly, random forest algorithm creates decision trees on data samples and then gets the prediction from each of them and finally selects the best solution by means of voting. As we know that a forest is made up of trees and more trees means more robust forest. ![]() But however, it is mainly used for classification problems. Random forest is a supervised learning algorithm which is used for both classification as well as regression.
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