infer_missing_with_probs
Train and use a machine learning model to predict (impute) the missing values in a column.
Non-missing values in a categorical target column will be used to train a prediction model (a Catboost classifier), which then predicts (imputes) the missing values. The step produces two output columns: one containing predicted classes for all rows, and a second containing a probability for each predicted class.
A dataset containing the column to be imputed as well as any other column to use as predictors in the model.
Name of the categorical column to impute. The step will predict the missing (and non-missing) class labels for this column, using rows in the dataset where the values are not missing to train the prediction model.
Predict non-missing values. When set to true, all values are predicted. Set this param to false to maintain original values when they are not missing.
Confidence threshold. Every prediction with probability strictly below this threshold will be set to NaN (missing).
Values must be in the following range:
0 ≤ threshold < 1
CatBoost configuration parameters. You can check the official documentation for more details about Catboost’s parameters here.
Depth of the tree.
Values must be in the following range:
2 ≤ depth ≤ 16
The method for processing missing values in the input dataset. Possible values:
-
“Forbidden”: Missing values are not supported, their presence is interpreted as an error.
-
“Min”: Missing values are processed as the minimum value (less than all other values) for the feature. It is guaranteed that a split that separates missing values from all other values is considered when selecting trees.
-
“Max”: Missing values are processed as the maximum value (greater than all other values) for the feature. It is guaranteed that a split that separates missing values from all other values is considered when selecting trees.
Using the Min or Max value of this parameter guarantees that a split between missing values and other values is considered when selecting a new split in the tree.
Values must be one of the following:
ForbiddenMinMax
Number of Iterations. The maximum number of trees that can be built when solving machine learning problems. When using other parameters that limit the number of iterations, the final number of trees may be less than the number specified in this parameter.
Values must be in the following range:
1 ≤ iterations < inf
Allow Writing Files. Allow to write analytical and snapshot files during training. If set to “false”, the snapshot and data visualization tools are unavailable.
The random seed used for training.
One Hot Max Size. Use one-hot encoding for all categorical features with a number of different values less than or equal to the given parameter value. Ctrs are not calculated for such features.
Values must be in the following range:
2 ≤ one_hot_max_size < inf
The maximum number of features that can be combined. Each resulting combination consists of one or more categorical features and can optionally contain binary features in the following form: “numeric feature > value”.
Values must be in the following range:
1 ≤ max_ctr_complexity < inf
Boosting type. Boosting scheme. Possible values are
- Ordered: Usually provides better quality on small datasets, but it may be slower than the Plain scheme. - Plain: The classic gradient boosting scheme.
Values must be one of the following:
OrderedPlain
Toggle encoding of feature columns.
When enabled, Graphext will auto-convert any column types to the numeric type before
fitting the model. How this conversion is done can be configured using the feature_encoder
option below.
Configures encoding of feature columns.
By default (null), Graphext chooses automatically how to convert any column types the model
may not understand natively to a numeric type.
A configuration object can be passed instead to overwrite specific parameter values with respect to their default values.
Numeric encoder. Configures encoding of numeric features.
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
Whether and how to impute (replace/fill) missing values.
Values must be one of the following:
MeanMedianMostFrequentConstNone
Whether and how to scale the final numerical values (across a single column).
Values must be one of the following:
StandardRobustKNNNone
Further parameters passed to the scaler function.
Details depend no the particular scaler used.
Boolean encoder. Configures encoding of boolean features.
Ordinal encoder. Configures encoding of categorical features that have a natural order.
Category encoder. May contain either a single configuration for all categorical variables, or two different configurations for low- and high-cardinality variables. For further details pick one of the two options below.
Multilabel encoder.
Configures encoding of multivalued categorical features (variable length lists of categories,
or the semantic type list[category] for short). May contain either a single configuration for
all multilabel variables, or two different configurations for low- and high-cardinality variables.
For further details pick one of the two options below.
Datetime encoder. Configures encoding of datetime (timestamp) features.
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
A list of numerical components to extract. Will create one numeric column for each component.
A list of cyclical time features to extract. “Cycles” are numerical transformations of features that should be represented on a circle. E.g. months, ranging from 1 to 12, should be arranged such that 12 and 1 are next to each other, rather than on opposite ends of a linear scale. We represent such cyclical time features on a circle by creating two columns for each original feature: the sin and cos of the numerical feature after appropriate scaling.
Whether to include the epoch as new feature (seconds since 01/01/1970).
Whether and how to impute (replace/fill) missing values.
Values must be one of the following:
MeanMedianMostFrequentConstNone
Whether and how to scale the final numerical values (across a single column).
Values must be one of the following:
StandardRobustKNNNone
How to scale the encoded (numerical columns).
Values must be one of the following:
EuclideanKNNNormNone
Embedding/vector encoder.
Configures encoding of multivalued numerical features (variable length lists of numbers, i.e. vectors, or the semantic type list[number] for short).
Text encoder. Configures encoding of text (natural language) features. Currently only allows Tf-Idf embeddings to represent texts. If you wish to use other embeddings, e.g. semantic, Word2Vec etc., transform your text column first using another step, and then use that result instead of the original texts.
include_text_features below to active it.Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
Parameters to be passed to the text encoder (Tf-Idf parameters only for now). See scikit-learn’s documentation for detailed parameters and their explanation.
How many output features to generate. The resulting Tf-Idf vectors will be reduced to these many dimensions (columns) using scikit-learn’s truncated SVD. This performs a kind of latent semantic analysis. By default we will reduce to 200 components.
Values must be in the following range:
2 ≤ n_components ≤ 1024
How to scale the encoded (numerical columns).
Values must be one of the following:
EuclideanKNNNormNone
Whether to include or ignore text columns during the processing of input data.
Enabling this will convert texts to their TfIdf representation. Each text will be
converted to an N-dimensional vector in which each component measures the relative
“over-representation” of a specific word (or n-gram) relative to its overall
frequency in the whole dataset. This is disabled by default because it will
often be better to convert texts explicitly using a previous step, such as
embed_text or embed_text_with_model.
Configure model validation. Allows evaluation of model performance via cross-validation with custom metrics. If not specified, will by default perform 5-fold cross-validation with automatically selected metrics.
Number of train-test splits to evaluate the model on.
Will split the dataset into training and test set n_splits times, train on the former
and evaluate on the latter using specified or automatically selected metrics.
What proportion of the data to use for testing in each split.
If null or not provided, will use cross-validation to split the dataset. E.g. if n_splits
is 5, the dataset will be split into 5 equal-sized parts. For five iterations four parts will then
be used for training and the remaining part for testing. If test_size is a number between 0 and 1,
in contrast, validation is done using a shuffle-split approach. Here, instead of splitting the data into
n_splits equal parts up front, in each iteration we randomize the data and sample a proportion equal
to test_size to use for evaluation and the remaining rows for training.
Values must be in the following range:
0 º test_size < 1
One or more metrics/scoring functions to evaluate the model with. When none is provided, will measure default metrics appropriate for the prediction task (classification vs. regression determined from model or type of target column). See sklearn model evaluation for further details.
Configure hypertuning. Configures the optimization of model hyper-parameters via cross-validated grid- or randomized search.
CatBoost configuration parameters. You can check the official documentation for more details about Catboost’s parameters here.
Depth of the tree.
Values must be in the following range:
2 ≤ depth ≤ 16
The method for processing missing values in the input dataset. Possible values:
-
“Forbidden”: Missing values are not supported, their presence is interpreted as an error.
-
“Min”: Missing values are processed as the minimum value (less than all other values) for the feature. It is guaranteed that a split that separates missing values from all other values is considered when selecting trees.
-
“Max”: Missing values are processed as the maximum value (greater than all other values) for the feature. It is guaranteed that a split that separates missing values from all other values is considered when selecting trees.
Using the Min or Max value of this parameter guarantees that a split between missing values and other values is considered when selecting a new split in the tree.
Values must be one of the following:
ForbiddenMinMax
Number of Iterations. The maximum number of trees that can be built when solving machine learning problems. When using other parameters that limit the number of iterations, the final number of trees may be less than the number specified in this parameter.
Values must be in the following range:
1 ≤ iterations < inf
Allow Writing Files. Allow to write analytical and snapshot files during training. If set to “false”, the snapshot and data visualization tools are unavailable.
The random seed used for training.
One Hot Max Size. Use one-hot encoding for all categorical features with a number of different values less than or equal to the given parameter value. Ctrs are not calculated for such features.
Values must be in the following range:
2 ≤ one_hot_max_size < inf
The maximum number of features that can be combined. Each resulting combination consists of one or more categorical features and can optionally contain binary features in the following form: “numeric feature > value”.
Values must be in the following range:
1 ≤ max_ctr_complexity < inf
Boosting type. Boosting scheme. Possible values are
- Ordered: Usually provides better quality on small datasets, but it may be slower than the Plain scheme. - Plain: The classic gradient boosting scheme.
Values must be one of the following:
OrderedPlain
Which search strategy to use for optimization.
Grid search explores all possible combinations of parameters specified in params.
Randomized search, on the other hand, randomly samples iterations parameter combinations
from the distributions specified in params.
Values must be one of the following:
gridrandom
How many randomly sampled parameter combinations to test in randomized search.
Values must be in the following range:
1 º iterations < inf
Configure model validation. Allows evaluation of model performance via cross-validation with custom metrics. If not specified, will by default perform 5-fold cross-validation with automatically selected metrics.
Number of train-test splits to evaluate the model on.
Will split the dataset into training and test set n_splits times, train on the former
and evaluate on the latter using specified or automatically selected metrics.
What proportion of the data to use for testing in each split.
If null or not provided, will use cross-validation to split the dataset. E.g. if n_splits
is 5, the dataset will be split into 5 equal-sized parts. For five iterations four parts will then
be used for training and the remaining part for testing. If test_size is a number between 0 and 1,
in contrast, validation is done using a shuffle-split approach. Here, instead of splitting the data into
n_splits equal parts up front, in each iteration we randomize the data and sample a proportion equal
to test_size to use for evaluation and the remaining rows for training.
Values must be in the following range:
0 º test_size < 1
One or more metrics/scoring functions to evaluate the model with. When none is provided, will measure default metrics appropriate for the prediction task (classification vs. regression determined from model or type of target column). See sklearn model evaluation for further details.
Metric used to select best model.
Values must be one of the following:
accuracy balanced_accuracy explained_variance f1_micro f1_macro f1_samples f1_weighted neg_mean_squared_error neg_median_absolute_error neg_root_mean_squared_error precision_micro precision_macro precision_samples precision_weighted recall_micro recall_macro recall_samples recall_weighted r2
Seed for random number generator ensuring reproducibility.
Values must be in the following range:
0 ≤ seed < inf
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