random forest

what are random forests?

• the action or process of classifying something.

• are an ensemble learning method for classification, regression

How does random forest calculate probability?

• In Random Forest package by passing parameter “type = prob” then instead of giving us the predicted class of the data point we get the probability.

• optimize a tuning parameter that governs the number of features that are randomly chosen to grow each tree from the bootstrapped data.

• Feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node

How do I stop Overfitting random forest?

• In Random Forest package by passing parameter “type = prob” then instead of giving us the predicted class of the data point we get the probability.

• optimize a tuning parameter that governs the number of features that are randomly chosen to grow each tree from the bootstrapped data.

• Feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node

How many decision trees are there in a random forest?

• multiple single trees each based on a random sample of the training data

• a random forest is a collection of decision trees

• none of them

random forest is supervised or unsupervised

• The random forest algorithm is a supervised learning model

• The random forest algorithm is unsupervised learning model

How feature importance is calculated in random forest?

• Feature importance is calculated as the decrease in node impurity weighted by the probability of reaching that node

• In Random Forest package by passing parameter “type = prob” then instead of giving us the predicted class of the data point we get the probability.

• optimize a tuning parameter that governs the number of features that are randomly chosen to grow each tree from the bootstrapped data.

How do you use the Random Forest in Python?

• Choose the number of trees you want in your algorithm and repeat steps 1 and 2.

• can be used for both classifications and regression task.

• Pick N random records from the dataset.

• Build a decision tree based on these N records.

How do you improve random forest accuracy?

• Algorithm Tuning

• Add more data

• putting less data

• Feature Selection