FastForestRegressionTrainer.Options Class

When a root split is impossible, allow training to proceed.

Percentage of training examples used in each bag. Default is 0.7 (70%).

Number of trees in each bag (0 for disabling bagging).

Bias for calculating gradient for each feature bin for a categorical feature.

Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.

Whether to do split based on multiple categorical feature values.

Compress the tree Ensemble.

Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose.

The entropy (regularization) coefficient between 0 and 1.

Column to use for example weight.

Print execution time breakdown to ML.NET channel.

Column to use for features.

The feature first use penalty coefficient.

Whether to collectivize features during dataset preparation to speed up training.

The fraction of features (chosen randomly) to use on each iteration. Use 0.9 if only 90% of features is needed. Lower numbers help reduce over-fitting.

The fraction of features (chosen randomly) to use on each split. If it's value is 0.9, 90% of all features would be dropped in expectation.

The feature re-use penalty (regularization) coefficient.

The seed of the active feature selection.

Tree fitting gain confidence requirement. Only consider a gain if its likelihood versus a random choice gain is above this value.

The number of histograms in the pool (between 2 and numLeaves).

Column to use for labels.

Maximum number of distinct values (bins) per feature.

Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.

Maximum categorical split points to consider when splitting on a categorical feature.

Print memory statistics to ML.NET channel.

The minimal number of data points required to form a new tree leaf.

Minimum categorical example percentage in a bin to consider for a split. Default is 0.1% of all training examples.

Minimum categorical example count in a bin to consider for a split.

The max number of leaves in each regression tree.

The number of data points to be sampled from each leaf to find the distribution of labels.

The number of threads to use.

Total number of decision trees to create in the ensemble.

Column to use for example groupId.

The seed of the random number generator.

Whether to shuffle the labels on every iteration.

Smoothing parameter for tree regularization.

The temperature of the randomized softmax distribution for choosing the feature.

Sparsity level needed to use sparse feature representation.

Calculate metric values for train/valid/test every k rounds.