Multiclass Logistic Regression component
This article describes a component in Azure Machine Learning designer.
Use this component to create a logistic regression model that can be used to predict multiple values.
Classification using logistic regression is a supervised learning method, and therefore requires a labeled dataset. You train the model by providing the model and the labeled dataset as an input to a component such as Train Model. The trained model can then be used to predict values for new input examples.
Azure Machine Learning also provides a Two-Class Logistic Regression component, which is suited for classification of binary or dichotomous variables.
About multiclass logistic regression
Logistic regression is a well-known method in statistics that is used to predict the probability of an outcome, and is popular for classification tasks. The algorithm predicts the probability of occurrence of an event by fitting data to a logistic function.
In multiclass logistic regression, the classifier can be used to predict multiple outcomes.
Configure a multiclass logistic regression
Add the Multiclass Logistic Regression component to the pipeline.
Specify how you want the model to be trained, by setting the Create trainer mode option.
Single Parameter: Use this option if you know how you want to configure the model, and provide a specific set of values as arguments.
Parameter Range: Select this option if you are not sure of the best parameters, and want to run a parameter sweep. Select a range of values to iterate over, and the Tune Model Hyperparameters iterates over all possible combinations of the settings you provided to determine the hyperparameters that produce the optimal results.
Optimization tolerance, specify the threshold value for optimizer convergence. If the improvement between iterations is less than the threshold, the algorithm stops and returns the current model.
L1 regularization weight, L2 regularization weight: Type a value to use for the regularization parameters L1 and L2. A non-zero value is recommended for both.
Regularization is a method for preventing overfitting by penalizing models with extreme coefficient values. Regularization works by adding the penalty that is associated with coefficient values to the error of the hypothesis. An accurate model with extreme coefficient values would be penalized more, but a less accurate model with more conservative values would be penalized less.
L1 and L2 regularization have different effects and uses. L1 can be applied to sparse models, which is useful when working with high-dimensional data. In contrast, L2 regularization is preferable for data that is not sparse. This algorithm supports a linear combination of L1 and L2 regularization values: that is, if
x = L1and
y = L2,
ax + by = cdefines the linear span of the regularization terms.
Different linear combinations of L1 and L2 terms have been devised for logistic regression models, such as elastic net regularization.
Random number seed: Type an integer value to use as the seed for the algorithm if you want the results to be repeatable over runs. Otherwise, a system clock value is used as the seed, which can produce slightly different results in runs of the same pipeline.
Connect a labeled dataset, and train the model:
If you set Create trainer mode to Single Parameter, connect a tagged dataset and the Train Model component.
If you set Create trainer mode to Parameter Range, connect a tagged dataset and train the model by using Tune Model Hyperparameters.
If you pass a parameter range to Train Model, it uses only the default value in the single parameter list.
If you pass a single set of parameter values to the Tune Model Hyperparameters component, when it expects a range of settings for each parameter, it ignores the values, and uses the default values for the learner.
If you select the Parameter Range option and enter a single value for any parameter, that single value you specified is used throughout the sweep, even if other parameters change across a range of values.
Submit the pipeline.
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