predict_proba
Apply the model to the given dataset to predict the probability that the object belongs to the given classes.
Method call format
predict_proba(data,
ntree_start=0,
ntree_end=0,
thread_count=-1,
verbose=None)
Parameters
Parameter | Possible types | Description | Default value |
---|---|---|---|
data |
| A file or matrix with the input dataset. | Required parameter |
ntree_start | int | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. | 0 |
ntree_end | int | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. | 0 (the index of the last tree to use equals to the number of trees in the model minus one) |
thread_count | int | The number of threads to use during training. Optimizes the speed of execution. This parameter doesn't affect results. | -1 (the number of threads is equal to the number of processor cores) |
verbose | bool | Output the measured evaluation metric to stderr. | None |
Parameter | Possible types | Description | Default value |
---|---|---|---|
data |
| A file or matrix with the input dataset. | Required parameter |
ntree_start | int | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to This parameter defines the index of the first tree to be used when applying the model or calculating the metrics (the inclusive left border of the range). Indices are zero-based. | 0 |
ntree_end | int | To reduce the number of trees to use when the model is applied or the metrics are calculated, set the range of the tree indices to This parameter defines the index of the first tree not to be used when applying the model or calculating the metrics (the exclusive right border of the range). Indices are zero-based. | 0 (the index of the last tree to use equals to the number of trees in the model minus one) |
thread_count | int | The number of threads to use during training. Optimizes the speed of execution. This parameter doesn't affect results. | -1 (the number of threads is equal to the number of processor cores) |
verbose | bool | Output the measured evaluation metric to stderr. | None |
Type of return value
numpy.array.