Random Forest is an ensemble technique that builds multiple:
- Decision trees
- Linear regression models
- Neural networks
- Support vector machines
Random Forest is an ensemble method that builds multiple decision trees. These decision trees are used to make predictions and are combined to create a more accurate and robust model. Random Forest is known for its ability to reduce overfitting and provide reliable predictions, making it a popular choice in machine learning.
Loading...
Related Quiz
- You're working for a company that generates vast amounts of log data daily. The company wants to analyze this data to gain insights into user behavior and system performance. Which Big Data tool would be most suitable for storing and processing this data efficiently?
- For applications requiring ACID transactions across multiple documents or tables, which database type would you lean towards?
- Which method for handling missing data involves using algorithms like k-NN to find similar records to impute the missing value?
- Which technique considers the spread of data points around the median to identify outliers?
- You're building a system that needs to store vast amounts of unstructured data, like user posts, images, and comments. Which type of database would be the best fit for this use case?