Machine Learning: Random Forest with Python from Scratch - Concluding remarks

What is a necessary condition for implementing a random forest algorithm?

• The dataset must be normalized.

• The dataset must be labeled.

• The dataset must be small.

• The dataset must be unlabeled.

Which of the following is NOT a step in data preprocessing for random forest?

• Normalizing the data

• Creating a single decision tree

• Filling missing values

• Removing outliers

What is the primary method used to determine the final prediction in a random forest?

• Majority voting

• Averaging predictions

• Using the largest tree

• Selecting the first prediction

What should you focus on if your random forest model is not performing well after several attempts?

• Increasing the number of trees

• Changing the algorithm

• Improving data preprocessing

• Reducing the dataset size

How does random forest compare to other algorithms in terms of data preprocessing sensitivity?

• It does not require preprocessing.

• It is more sensitive.

• It is less sensitive.

• It is equally sensitive.

How can random forest be used for regression tasks?

• By using a different dataset

• By using random forest regressor

• By increasing the number of trees

• By changing the voting method

Is it possible to apply random forest to multiclass classification problems?

• No, it requires a different dataset.

• Yes, but with a different algorithm.

• Yes, using the same method as binary classification.

• No, it only works for binary classification.