![]() So then hyperparameter optimization is the process of finding the right combination of hyperparameter values to achieve maximum performance on the data in a reasonable amount of time. These parameters are tunable and can directly affect how well a model trains. In short, hyperparameters are different parameter values that are used to control the learning process and have a significant effect on the performance of machine learning models.Īn example of hyperparameters in the Random Forest algorithm is the number of estimators ( n_estimators), maximum depth ( max_depth), and criterion. What is hyperparameter optimization?īefore I define hyperparameter optimization, you need to understand what a hyperparameter is. This task always comes after the model selection process where you choose the model that is performing better than other models. One of the steps you have to perform is hyperparameter optimization on your selected model. When working on a machine learning project, you need to follow a series of steps until you reach your goal. fit ( training ) // Prepare test documents, which are unlabeled. setNumFolds ( 2 ) // Use 3+ in practice // Run cross-validation, and choose the best set of parameters. setEvaluator ( new Binar圜lassificationEvaluator ()). CrossValidator cv = new CrossValidator (). Note that the evaluator here is a Binar圜lassificationEvaluator and its default metric // is areaUnderROC. A CrossValidator requires an Estimator, a set of Estimator ParamMaps, and an Evaluator. This will allow us to jointly choose parameters for all Pipeline stages. build () // We now treat the Pipeline as an Estimator, wrapping it in a CrossValidator instance. Import .Pipeline import .classification.LogisticRegression import .evaluation.Binar圜lassificationEvaluator import .feature. However, it is also a well-established method for choosing parameters which is more statistically sound than heuristic hand-tuning. In other words, using CrossValidator can be very expensive. In realistic settings, it can be common to try many more parameters and use more folds ( $k=3$ and $k=10$ are common). This multiplies out to $(3 \times 2) \times 2 = 12$ different models being trained. Note that cross-validation over a grid of parameters is expensive.Į.g., in the example below, the parameter grid has 3 values for hashingTF.numFeatures and 2 values for lr.regParam, and CrossValidator uses 2 folds. The following example demonstrates using CrossValidator to select from a grid of parameters. To evaluate a particular ParamMap, CrossValidator computes the average evaluation metric for the 3 Models produced by fitting the Estimator on the 3 different (training, test) dataset pairs.Īfter identifying the best ParamMap, CrossValidator finally re-fits the Estimator using the best ParamMap and the entire dataset.Įxamples: model selection via cross-validation E.g., with $k=3$ folds, CrossValidator will generate 3 (training, test) dataset pairs, each of which uses 2/3 of the data for training and 1/3 for testing. Cross-ValidationĬrossValidator begins by splitting the dataset into a set of folds which are used as separate training and test datasets. ![]() To help construct the parameter grid, users can use the ParamGridBuilder utility. The default metric used to choose the best ParamMap can be overridden by the setMetricName The Evaluator can be a RegressionEvaluatorįor regression problems, a Binar圜lassificationEvaluatorįor binary data, or a MulticlassClassificationEvaluatorįor multiclass problems.
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