Regressors
Supported Regressors
Linear Regression
Simple and interpretable
Suitable for linear relationships between features and targets
Includes Logistic Regression for classification tasks
Random Forest
Ensemble method using multiple decision trees
Handles non-linear relationships well
Provides feature importance rankings
Gradient Boosting
Builds an ensemble of weak learners sequentially
Often provides high accuracy
AdaBoost
Adaptive Boosting algorithm
Focuses on hard-to-classify instances
Works well with weak learners
Neural Networks
Flexible architecture for complex patterns
Supports both regression and classification
Includes options for deep learning
Multi-output Support
All our regressors support multi-output scenarios, allowing prediction of multiple targets simultaneously.
Auto Mode
Each regressor includes an "auto mode" that performs automated hyperparameter tuning to optimize model performance.
Usage
To use a specific regressor, set the regressor option in your model configuration.
For more detailed information about each regressor, including its specific parameters, strengths, and use cases, please refer to the individual documentation.
"Guess the Candies"
Imagine we're trying to guess how many candies are in a jar. We have information about the jar's height, width, and weight.
Here's how each regressor might approach this problem:
Linear Regression: This is like drawing a straight line through our data points. It might say, "For every inch taller the jar is, add 10 candies. For every inch wider, add 15 candies. For every ounce heavier, add 5 candies." It's simple but might miss some complex relationships.
Random Forest: This is like asking a bunch of friends to guess, each using slightly different rules, then taking the average of all their guesses. One friend might focus more on the height, another on the weight, and so on. By combining all these guesses, we often get a pretty good estimate.
Gradient Boosting: This is like guessing, then looking at where we went wrong, and making a new rule to fix those mistakes. We keep doing this, making new rules to fix the remaining errors, until our guesses get really good.
AdaBoost: This is similar to Gradient Boosting, but it pays special attention to the jars we guessed really badly on. It's like saying, "Oops, we were way off on that tall, skinny jar. Let's make sure we have a special rule for jars like that."
Neural Networks: This is like having a super-smart friend who looks at all the jars and candies, and comes up with their own complex method for guessing. We don't always know exactly how they're doing it, but their guesses are often very accurate, especially if we have lots of jars to learn from.
Each method has its strengths, and the best choice often depends on how many jars we've seen before, how complex the relationship between jar features and candy count is, and how much time we have to make our guesses.
Last updated