Github user manishamde commented on a diff in the pull request: https://github.com/apache/spark/pull/3461#discussion_r21133672 --- Diff: docs/mllib-decision-tree.md --- @@ -103,36 +106,73 @@ and the resulting `$M-1$` split candidates are considered. ### Stopping rule -The recursive tree construction is stopped at a node when one of the two conditions is met: +The recursive tree construction is stopped at a node when one of the following conditions is met: 1. The node depth is equal to the `maxDepth` training parameter. -2. No split candidate leads to an information gain at the node. +2. No split candidate leads to an information gain greater than `minInfoGain`. +3. No split candidate produces child nodes which each have at least `minInstancesPerNode` training instances. + +## Usage tips + +We include a few guidelines for using decision trees by discussing the various parameters. +There are many parameters, put in order here with the most imporant first. New users should mainly consider the "Problem specification parameters" section below and the `maxDepth` parameter. + +### Problem specification parameters + +These parameters describe the problem you want to solve and your dataset. +They should be specified and do not require tuning. + +* **`algo`**: `Classification` or `Regression` + +* **`numClasses`**: Number of classes (for `Classification` only) + +* **`categoricalFeaturesInfo`**: Specifies which features are categorical and how many categorical values each of those features can take. This is given as a map from feature indices to feature arity (number of categories). Any features not in this map are treated as continuous. + * E.g., `Map(1 -> 2, 4 -> 10)` specifies that feature `1` is binary (taking values `0` or `1`) and that feature `4` has 10 categories (values `{0, 1, ..., 9}`). Note that feature indices are 0-based: features `1` and `4` are the 2nd and 5th elements of an instance's feature vector. + * Note that you do not have to specify `categoricalFeaturesInfo`. The algorithm will still run and may get reasonable results. However, performance should be better if categorical features are properly designated. + +### Stopping criteria + +These parameters determine when the tree stops building (adding new nodes). +These parameters may be tuned. Be careful to validate on held-out test data when tuning in order to avoid overfitting. + +* **`maxDepth`**: Maximum depth of a tree. Deeper trees are more expressive (potentially allowing higher accuracy), but deeper trees are also more costly to train and are more likely to overfit. + +* **`minInstancesPerNode`**: For a node to be split further, each of its children must receive at least this number of training instances. This is commonly used with [Random Forests](mllib-random-forest.html) since those are often trained deeper than individual trees. + +* **`minInfoGain`**: For a node to be split further, the split must improve at least this much (in terms of information gain). + +### Tunable parameters -## Implementation details +These parameters may be tuned. Be careful to validate on held-out test data when tuning in order to avoid overfitting. -### Max memory requirements +* **`maxBins`**: Number of bins used when discretizing continuous features. + * Increasing `maxBins` allows the algorithm to consider more split candidates and make fine-grained split decisions. However, it also increases computation and communication. + * Note that the `maxBins` parameter must be at least the maximum number of categories `$M$` for any categorical feature. -For faster processing, the decision tree algorithm performs simultaneous histogram computations for -all nodes at each level of the tree. This could lead to high memory requirements at deeper levels -of the tree, potentially leading to memory overflow errors. To alleviate this problem, a `maxMemoryInMB` -training parameter specifies the maximum amount of memory at the workers (twice as much at the -master) to be allocated to the histogram computation. The default value is conservatively chosen to -be 256 MB to allow the decision algorithm to work in most scenarios. Once the memory requirements -for a level-wise computation cross the `maxMemoryInMB` threshold, the node training tasks at each -subsequent level are split into smaller tasks. +* **`maxMemoryInMB`**: Amount of memory to be used for collecting sufficient statistics. + * The default value is conservatively chosen to be 256 MB to allow the decision algorithm to work in most scenarios. Increasing `maxMemoryInMB` can lead to faster training (if the memory is available) by allowing fewer passes over the data. However, there may be decreasing returns as `maxMemoryInMB` grows since the amount of communication on each iteration can be proportional to `maxMemoryInMB`. --- End diff -- Ok.
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