Stochastic Gradient Descent
Minimize an objective function using a stochastic approximation of gradient descent.
Inputs
 Data: input dataset
 Preprocessor: preprocessing method(s)
Outputs
 Learner: stochastic gradient descent learning algorithm
 Model: trained model
The Stochastic Gradient Descent widget uses stochastic gradient descent that minimizes a chosen loss function with a linear function. The algorithm approximates a true gradient by considering one sample at a time, and simultaneously updates the model based on the gradient of the loss function. For regression, it returns predictors as minimizers of the sum, i.e. Mestimators, and is especially useful for largescale and sparse datasets.

Specify the name of the model. The default name is "SGD".

Algorithm parameters:
 Classification loss function:
 Hinge (linear SVM)
 Logistic Regression (logistic regression SGD)
 Modified Huber (smooth loss that brings tolerance to outliers as well as probability estimates)
 Squared Hinge (quadratically penalized hinge)
 Perceptron (linear loss used by the perceptron algorithm)
 Squared Loss (fitted to ordinary leastsquares)
 Huber (switches to linear loss beyond ε)
 Epsilon insensitive (ignores errors within ε, linear beyond it)
 Squared epsilon insensitive (loss is squared beyond εregion).
 Regression loss function:
 Squared Loss (fitted to ordinary leastsquares)
 Huber (switches to linear loss beyond ε)
 Epsilon insensitive (ignores errors within ε, linear beyond it)
 Squared epsilon insensitive (loss is squared beyond εregion).
 Classification loss function:

Regularization norms to prevent overfitting:
 None.
 Lasso (L1) (L1 leading to sparse solutions)
 Ridge (L2) (L2, standard regularizer)
 Elastic net (mixing both penalty norms).
Regularization strength defines how much regularization will be applied (the less we regularize, the more we allow the model to fit the data) and the mixing parameter what the ratio between L1 and L2 loss will be (if set to 0 then the loss is L2, if set to 1 then it is L1).

Learning parameters.
 Learning rate:
 Constant: learning rate stays the same through all epochs (passes)
 Optimal: a heuristic proposed by Leon Bottou
 Inverse scaling: earning rate is inversely related to the number of iterations
 Initial learning rate.
 Inverse scaling exponent: learning rate decay.
 Number of iterations: the number of passes through the training data.
 If Shuffle data after each iteration is on, the order of data instances is mixed after each pass.
 If Fixed seed for random shuffling is on, the algorithm will use a fixed random seed and enable replicating the results.
 Learning rate:

Produce a report.

Press Apply to commit changes. Alternatively, tick the box on the left side of the Apply button and changes will be communicated automatically.
Preprocessing
SGD uses default preprocessing when no other preprocessors are given. It executes them in the following order:
 removes instances with unknown target values
 continuizes categorical variables (with onehotencoding)
 removes empty columns
 imputes missing values with mean values
 normalizes the data by centering to mean and scaling to standard deviation of 1
To remove default preprocessing, connect an empty Preprocess widget to the learner.
Feature Scoring
Stochastic Gradient Descent can be used with Rank for feature scoring. See Learners as Scorers for an example.
Examples
For the classification task, we will use iris dataset and test two models on it. We connected Stochastic Gradient Descent and Tree to Test & Score. We also connected File to Test & Score and observed model performance in the widget.
For the regression task, we will compare three different models to see which predict what kind of results. For the purpose of this example, the housing dataset is used. We connect the File widget to Stochastic Gradient Descent, Linear Regression and kNN widget and all four to the Predictions widget.