Predict with data. Parameters ---------- data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series or polars.DataFrame or polars.Series or catboost.FeaturesData Data to apply model on.
(self, data, prediction_type=None, ntree_start=0, ntree_end=0, thread_count=-1, verbose=None, task_type="CPU")
| 6181 | save_snapshot, snapshot_file, snapshot_interval, init_model, callbacks, log_cout, log_cerr) |
| 6182 | |
| 6183 | def predict(self, data, prediction_type=None, ntree_start=0, ntree_end=0, thread_count=-1, verbose=None, task_type="CPU"): |
| 6184 | """ |
| 6185 | Predict with data. |
| 6186 | |
| 6187 | Parameters |
| 6188 | ---------- |
| 6189 | data : catboost.Pool or list of features or list of lists or numpy.ndarray or pandas.DataFrame or pandas.Series |
| 6190 | or polars.DataFrame or polars.Series or catboost.FeaturesData |
| 6191 | Data to apply model on. |
| 6192 | If data is a simple list (not list of lists) or a one-dimensional numpy.ndarray it is interpreted |
| 6193 | as a list of features for a single object. |
| 6194 | |
| 6195 | prediction_type : string, optional (default='RawFormulaVal') |
| 6196 | Can be: |
| 6197 | - 'RawFormulaVal' : return raw formula value. |
| 6198 | - 'Exponent' : return Exponent of raw formula value. |
| 6199 | |
| 6200 | ntree_start: int, optional (default=0) |
| 6201 | Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). |
| 6202 | |
| 6203 | ntree_end: int, optional (default=0) |
| 6204 | Model is applied on the interval [ntree_start, ntree_end) (zero-based indexing). |
| 6205 | If value equals to 0 this parameter is ignored and ntree_end equal to tree_count_. |
| 6206 | |
| 6207 | thread_count : int (default=-1) |
| 6208 | The number of threads to use when applying the model. |
| 6209 | Allows you to optimize the speed of execution. This parameter doesn't affect results. |
| 6210 | If -1, then the number of threads is set to the number of CPU cores. |
| 6211 | |
| 6212 | verbose : bool |
| 6213 | If True, writes the evaluation metric measured set to stderr. |
| 6214 | |
| 6215 | task_type : string, [default=None] |
| 6216 | The evaluator type. |
| 6217 | Possible values: |
| 6218 | - 'CPU' |
| 6219 | - 'GPU' (models with only numerical features are supported for now) |
| 6220 | |
| 6221 | Returns |
| 6222 | ------- |
| 6223 | prediction : |
| 6224 | If data is for a single object, the return value is single float formula return value |
| 6225 | otherwise one-dimensional numpy.ndarray of formula return values for each object. |
| 6226 | """ |
| 6227 | if prediction_type is None: |
| 6228 | prediction_type = self._get_default_prediction_type() |
| 6229 | return self._predict(data, prediction_type, ntree_start, ntree_end, thread_count, verbose, 'predict', task_type) |
| 6230 | |
| 6231 | def staged_predict(self, data, prediction_type='RawFormulaVal', ntree_start=0, ntree_end=0, eval_period=1, thread_count=-1, verbose=None): |
| 6232 | """ |