Random forests is a supervised learning algorithm. It can be used both for classification and regression. It is also the most flexible and easy to use algorithm. A forest is comprised of trees. It is said that the more trees it has, the more robust a forest is. Random forests creates decision trees on randomly selected data samples, gets prediction from each tree and selects the best solution by means of voting. It also provides a pretty good indicator of the feature importance.

It works in four steps:

• Select random samples from a given dataset.
• Construct a decision tree for each sample and get a prediction result from each decision tree.
• Perform a vote for each predicted result.
• Select the prediction result with the most votes as the final prediction.

https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html

• min_samples_split (default value = 2)
nodes cannot be further seperated below this value.
• criterion : optional (default=”gini”)
It controls how a Decision Tree decides where to split the data. It is the measure of impurity in a bunch of examples. This parameter allows us to use the different-different attribute selection measure. Supported criteria are “gini” for the Gini index and “entropy” for the information gain.
• n_estimators(default value = 100
This parameter tells is the number of trees in the forest.

         precision    recall  f1-score   support

      0       1.00      1.00      1.00        14
      1       0.94      0.94      0.94        17
      2       0.93      0.93      0.93        14
avg / total   0.96      0.96      0.96        45

        precision    recall  f1-score   support

      0       1.00      1.00      1.00        14
      1       1.00      0.94      0.97        17
      2       0.93      1.00      0.97        14
avg / total   0.98      0.98      0.98        45

Finding important features or selecting features in the IRIS dataset.