Classification Tool
Use the Classification tool as part of a machine-learning pipeline to identify what category a target belongs to. The tool provides several algorithms you can use to train a model. The tool also allows you to tune a model using many parameters.
Alteryx Intelligence Suite Required
This tool is part of Alteryx Intelligence Suite. Intelligence Suite requires a separate license and add-on installer to Designer. After you install Designer, install Intelligence Suite and start your free trial.
Configure the Tool
This section contains info about how to configure the Classification tool.
Select Algorithm
Select what algorithm you want to use. You can choose Logistic Regression, Decision Tree, Random Forest, or XGBoost.
Configure Parameters
Configure the parameters. Each algorithm has different parameters from other algorithms. 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 in-depth understanding of what they do.
Reference the table for each algorithm to see what parameters do:
Name | Description | Options | Default |
Class Weight | Class Weight assigns weights to different classes in the dataset. Some algorithms overvalue prevailing classes, resulting in imbalances. This parameter helps balance classes in the dataset by assigning additional weight to minority classes. |
| None |
Fit Intercept | Decide whether you want the algorithm to calculate the intercept for your linear-regression model. Also known as the "constant," the intercept is the expected mean value of y where x equals 0. |
| Scale Intercept: 1.0 |
Max Iterations | Max Iterations specifies the maximum number of iterations to allow solvers to converge. Models with more iterations capture more information about the data. | Any integer. | 100 |
Multiclass | Multiclass specifies whether the algorithm needs to handle more than two classes. |
| Auto |
Penalty | Penalty, also known as "regularization," refers to the practice of modifying the loss function to penalize certain values that the model would overvalue otherwise. L1 (Lasso Regression) and L2 (Ridge Regression) are two methods of specifying the penalty term. |
| L2 |
Problem Formulation | Problem Formulation transforms a primal optimization problem into a dual problem. |
*You can only use Dual if you select the L2 option for Penalty and Liblinear for Solver. | Primal |
Random Seed | Random Seed specifies the starting number for generating a pseudorandom sequence. If you select None, a random-number generator picks a starting number. |
| Seed:10 |
Solver | Solver is the method the logistic-regression uses to optimize its curve to best fit the data by determining sigmoid weights. |
| Liblinear |
Tolerance | Tolerance sets the stopping criteria for when the algorithm should detect that parameters are close enough to convergence (in other words, remain constant). | Any positive float. | .0001 |
Tuner | Regularization Tuner (C) allows you to adjust the amount of penalty (in other words, regularization) you apply, effectively limiting features that are heavily weighted by the model. Format this parameter as a positive float. | Any positive float. | 1.0 |
Name | Description | Options | Default |
Class Weight | Class Weight assigns weights to the different classes in the dataset. |
| None |
Criterion | Use the Criterion parameter to select a method to measure how well the decision-tree algorithm splits your data into different nodes. |
| Gini Impurity |
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. |
| Unlimited |
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 best-first 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. 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 decision-tree algorithm assumes equal weights by default. | Any float. | 0.0 |
Random Seed | Random Seed specifies the starting number for generating a pseudorandom sequence. If you select None, a random-number generator picks a starting number. |
| Seed: 10 |
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 |
Class Weight | Class Weight assigns weights to the different classes in the dataset. Random-forest algorithms tend to overvalue prevailing classes, resulting in imbalances. Class Weight helps balance classes in the dataset by assigning additional weight to minority classes. Balancing classes can improve model performance. By default, all classes have a weight of 1. |
| None |
Criterion | Use the Criterion parameter to select a method to measure how well the random-forest algorithm splits your data into different nodes. |
| Gini Impurity |
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 capture more information about the data. |
| Limited: 10 |
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 random-forest 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 random-number generator picks a starting number. |
| Seed: 10 |
Name | Description | Options | Default |
Sample Columns by Level | Sample Features by Level is the percentage of data that the algorithm randomly creates a subsample for each depth level in a tree. | Any float from 0 to 1. | 1 |
Sample Columns by Node | Sample Features by Node is the percentage of data that the algorithm randomly creates a subsample for each node in a tree. | Any float from 0 to 1. | 1 |
Sample Columns by Tree | Sample Features by Tree is the percentage of data that the algorithm randomly creates a subsample for each tree. | Any float from 0 to 1. | 1 |
Gamma | Gamma sets the loss reduction required for a decision tree to split into a new node. So a split occurs where it would reduce loss by an amount equal to or greater than Gamma. | Any positive integer or 0. | 0 |
Learning Rate | Learning Rate is the rate at which the algorithm lets new info override old info. Usually you set Learning Rate in logarithmic increments (for example, 0.003, 0.03, 0.3). | Any float from 0 to 1. | 0.05 |
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 capture more information about the data. | Any number equal to or greater than 1. | 3 |
Minimum Child Weight | Minimum Child Weight sets the threshold of Hessian weight required for a decision tree to split into a new node. So a split occurs where it would decrease the Hessian weight by an amount equal to or greater than Minimum Child Weight. | Any positive number or 0. | 1 |
Number of Estimators | Number of Estimators is the number of trees you want to create as part of the forest. | Any number equal to or greater than 1. | 100 |
Random Seed | Random Seed specifies the starting number for generating a pseudorandom sequence. | Any whole number. | 10 |
Subsample | Subsample is the percentage of data that the algorithm randomly creates a subsample of. | Any number from 0 to 1. | 1 |