Regression Tool
Use the Regression tool as part of a machinelearning pipeline to identify a trend. The tool provides several algorithms you can use to train a model. The tool also allows you to tune a model using many parameters.
Configure the Tool
This section contains info about how to configure the Regression tool.
Select Algorithm
Select what algorithm you want to use. You can choose Linear Regression, Decision Tree, or Random Forest.
Configure Parameters
Configure the parameters. Each algorithm has specific parameters. Each algorithm also has both general and advanced parameters. General parameters are integral to creating an accurate model, even for beginners. Advanced parameters might improve accuracy, but require indepth understanding of what they do.
Reference the table for each algorithm to see what parameters do:
Name  Description  Options  Default 
Fit Intercept  Decide whether you want the algorithm to calculate the intercept for your linearregression model. Also known as the “constant,” the intercept is the expected mean value of y where x equals 0. 

On 
Normalize  Decide whether you want the algorithm to normalize your targets. Normalization adjusts your targets in such a way that you can compare them on a common scale with other data, which may help you identify associations in your data. 

On 
Name  Description  Options  Default 
Bootstrap  Bootstrapping, the foundation of bagging, is a method used to sample the dataset for purposes of training. This method involves iteratively creating subsamples of your dataset to simulate new, unseen data, which you can use to improve the generalizability of your model. 

On 
Criterion  Use the Criterion parameter to select a method to measure how well the decisiontree algorithm splits your data into different nodes. 

Mean Squared Error (MSE) 
Max Depth  Max Depth is the longest path from a root to a leaf of a tree. Deeper trees have more splits and capture more information about the data. 

Limited: 100 
Max Features  Max Features sets the maximum number of features your decision tree considers when looking for a best first split. 

Auto 
Max Leaf Nodes  Max Leaf Nodes is the upward limit on the total number of leaf nodes your algorithm can generate. It grows nodes up to the maximum number in a bestfirst manner. The algorithm determines what nodes are best based on their capacity for impurity reduction. Use the Criterion parameter to specify how you want to measure impurity reduction.  Any integer or None.  None 
Min Impurity Decrease  Min Impurity Decrease sets the minimum threshold of impurity reduction required for the decision tree to split into a new node. So a split occurs where it would decrease impurity by an amount equal to or greater than Min Impurity Decrease, a split occurs. Use the Criterion parameter to specify how you want to measure impurity reduction.  Any float.  0.0 
Min Samples Split  Min Samples Split sets the minimum threshold of samples required for the decision tree to split into a new node. The algorithm can consider as few as one sample or as many as all samples.  Any integer or fraction.  Integer: 2 
Min Weight Fraction Leaf  Min Weight Fraction Leaf is the minimum threshold of weight required for the decision tree to split into a new node. That threshold is equal to the minimum fraction of the total weights for all samples. The decisiontree algorithm assumes equal weights by default.  Any float.  0.0 
Presort  Use this parameter to presort the data, which might help the algorithm find best splits faster. 

Off 
Random Seed  Random Seed specifies the starting number for generating a pseudorandom sequence. If you select None, a randomnumber generator picks a starting number. 

None 
Splitter  Splitter is the strategy used for splitting at a node. It includes options for the best first split and the best random split. The algorithm determines what nodes are best based on their capacity for impurity reduction. 

Best 
Name  Description  Options  Default 
Bootstrap  Bootstrapping, the foundation of bagging, is a method used to sample the dataset for purposes of training. This method involves iteratively creating subsamples of your dataset to simulate new, unseen data, which you can use to improve the generalizability of your model. 

On 
Criterion  Use the Criterion parameter to select a method to measure how well the randomforest algorithm splits your data into different nodes, which comprise the many different trees in your random forest. 

Mean Squared Error (MSE) 
Max Depth  Max Depth is the longest path from a root to a leaf for each tree in the forest. Deeper trees have more splits and captures more information about the data. 

Unlimited 
Max Features  Max Features sets the maximum number of features each decision tree in the forest considers when looking for a best first split. 

Auto 
Min Impurity Decrease  Min Impurity Decrease sets the minimum threshold of impurity reduction required for a decision tree to split into a new node. So a split occurs where it would decrease impurity by an amount equal to or greater than Min Impurity Decrease. Use the Criterion parameter to specify how you want to measure impurity reduction.  Any float.  0.0 
Min Samples Split  Min Samples Split sets the minimum threshold of samples required for the decision tree (in a random forest) to split into a new node. The algorithm can consider as few as one sample or as many as all samples.  Any integer or fraction.  Integer: 2 
Min Weight Fraction Leaf  Min Weight Fraction Leaf is the minimum threshold of weight required for a decision tree to split into a new node. That threshold is equal to the minimum fraction of the total weights for all samples. The randomforest algorithm assumes equal weights by default.  Any float.  0.0 
Number of Estimators  Number of Estimators is the number of trees you want to create as part of the forest.  Any integer.  100 
Random Seed  Random Seed specifies the starting number for generating a pseudorandom sequence. If you select None, a randomnumber generator picks a starting number. 

None 