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The output will consist of a new column with the trained model’s predictions on the training data, as well as a saved and named model file that can be used in other projects for prediction of new data. Optionally, if a second output column name is provided, the model’s predicted probabilities will also be returned. A detailed guide on how to configure this step for model tuning and performance evaluation can be found here.

Usage

The following examples show how the step can be used in a recipe.

Examples

  • Example 1
  • Example 2
  • Example 3
  • Signature
Train a classification model with default parameters. By default, a Catboost model will be trained, but this can be changed to any of the supported models by specifying the model parameter (see below for details):
train_classification(ds, {"target": "class"}) -> (ds.predicted, model)

Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").
ds
dataset
required
Should contain the target column and the feature columns you wish to use in the model.
*predicted
column
One or two columns containing the model’s predictions. If two column names are provided, the second column will contain the model’s predicted probabilities.
model
file[model_classification[ds]]
required
Zip file containing the trained model and associated information.
info
file.hidden
required

Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

  • CatboostClassifier
model
string
default:"CatboostClassifier"
Train a Catboost classifier. I.e. gradient boosted decision trees with support for categorical variables and missing values.
target
string (ds.column:category|boolean)
required
Target variable (labels). Name of the column that contains your target values (labels).
positive_class
[string, null]
Name of the positive class. In binary classification, usually the class you’re most interested in, for example the label/class corresponding to successful lead conversion in a lead score model, the class corresponding to a customer who has churned in a churn prediction model, etc.If provided, will automaticall measure the performance (accuracy, precision, recall) of the model on this class, in addition to averages across all classes. If not provided, only summary metrics will be reported.
max_classes
integer
default:"10"
Maximum number of classes in the target variable. If there are more classes than this, the least frequent classes will be grouped together into a single class called “others”. Reducing the number of classes in the target variable can help improve model performance, especially when the number of classes is very large, some classes are very rare, or the dataset doesn’t have sufficient samples for all classes. Raising this significantly might lead to much longer training times.Values must be in the following range:
2max_classes100
feature_importance
[string, boolean, null]
default:"native"
Importance of each feature in the model. Whether and how to measure each feature’s contribution to the model’s predictions. The higher the value, the more important the feature was in the model. Only relative values are meaningful, i.e. the importance of a feature relative to other features in the model.Also note that feature importance is usually meaningful only for models that fit the data well.The default (null, true or "native") uses the classifier’s native feature importance measure, e.g. prediction-value-change in the case of Catboost, Gini importance in the case of scikit-learn’s DecisionTreeClassifier, and the mean of absolute coefficients in the case of logistic regression.When set to "permutation", uses permutation importance, i.e. measures the decrease in model score when a single feature’s values are randomly shuffled. This is considerably slower than native feature importance (the model needs to be evaluated an additional k*n times, where k is the number of features and n the number of repetitions to average over). On the positive side it is model-agnostic and doesn’t suffer from bias towards high cardinality features (like some tree-based feature importances). On the negative side, it can be sensitive to strongly correlated features, as the unshuffled correlated variable is still available to the model when shuffling the original variable.When set to false, no feature importance will be calculated.Values must be one of the following:
  • True
  • False
  • native
  • permutation
  • null
encode_features
boolean
default:"true"
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.
If disabled, any model trained in this step will assume that input data is already in an appropriate format (e.g. numerical and not containing any missing values).
feature_encoder
[null, object]
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.
number
object
Numeric encoder. Configures encoding of numeric features.
indicate_missing
boolean
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
imputer
[null, string]
Whether and how to impute (replace/fill) missing values.Values must be one of the following:
  • Mean
  • Median
  • MostFrequent
  • Const
  • None
scaler
[null, string]
Whether and how to scale the final numerical values (across a single column).Values must be one of the following:
  • Standard
  • Robust
  • KNN
  • None
scaler_params
object
Further parameters passed to the scaler function. Details depend no the particular scaler used.
bool
object
Boolean encoder. Configures encoding of boolean features.
indicate_missing
boolean
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
imputer
[null, string]
Whether and how to impute (replace/fill) missing values.Values must be one of the following:
  • MostFrequent
  • Const
  • None
ordinal
object
Ordinal encoder. Configures encoding of categorical features that have a natural order.
indicate_missing
boolean
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
imputer
[null, string]
Whether and how to impute (replace/fill) missing values.Values must be one of the following:
  • MostFrequent
  • Const
  • None
category
[object, object]
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.
  • Simple category encoder
  • Conditional category encoder
indicate_missing
boolean
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
imputer
[null, string]
Whether and how to impute (replace/fill) missing values.Values must be one of the following:
  • MostFrequent
  • Const
  • None
max_categories
[null, integer]
Maximum number of unique categories to encode. Only the N-1 most common categories will be encoded, and the rest will be grouped into a single “Others” category.Values must be in the following range:
1max_categories < inf
encoder
[null, string]
How to encode categories.Values must be one of the following:OneHot Label Ordinal Binary Frequency None
scaler
[null, string]
Whether and how to scale the final numerical values (across a single column).Values must be one of the following:
  • Standard
  • Robust
  • KNN
  • None
multilabel
[object, object]
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.
  • Simple multilabel encoder
  • Conditional multilabel encoder
indicate_missing
boolean
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
encoder
[null, string]
How to encode categories/labels in multilabel (list[category]) columns.Values must be one of the following:
  • Binarizer
  • TfIdf
  • None
max_categories
[null, integer]
Maximum number of categories/labels to encode. If a number is provided, the result of the encoding will be reduced to these many dimensions (columns) using scikit-learn’s truncated SVD. When applied together with (after a) Tf-Idf encoding, this performs a kind of latent semantic analysis.Values must be in the following range:
2max_categories < inf
scaler
[null, string]
How to scale the encoded (numerical columns).Values must be one of the following:
  • Euclidean
  • KNN
  • Norm
  • None
datetime
object
Datetime encoder. Configures encoding of datetime (timestamp) features.
indicate_missing
boolean
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
components
array[string]
A list of numerical components to extract. Will create one numeric column for each component.
Item
string
Each item in array.Values must be one of the following:day dayofweek dayofyear hour minute month quarter season second week weekday weekofyear year
cycles
array[string]
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.
Item
string
Each item in array.Values must be one of the following:
  • day
  • dayofweek
  • dayofyear
  • hour
  • month
epoch
[null, boolean]
Whether to include the epoch as new feature (seconds since 01/01/1970).
imputer
[null, string]
Whether and how to impute (replace/fill) missing values.Values must be one of the following:
  • Mean
  • Median
  • MostFrequent
  • Const
  • None
component_scaler
[null, string]
Whether and how to scale the final numerical values (across a single column).Values must be one of the following:
  • Standard
  • Robust
  • KNN
  • None
vector_scaler
[null, string]
How to scale the encoded (numerical columns).Values must be one of the following:
  • Euclidean
  • KNN
  • Norm
  • None
embedding
object
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).
indicate_missing
boolean
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
scaler
[null, string]
How to scale the encoded (numerical columns).Values must be one of the following:
  • Euclidean
  • KNN
  • Norm
  • None
text
object
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.
Texts are excluded by default from the overall encoding of the dataset. See parameter include_text_features below to active it.
indicate_missing
boolean
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
encoder_params
object
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.
n_components
integer
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:
2n_components1024
scaler
[null, string]
How to scale the encoded (numerical columns).Values must be one of the following:
  • Euclidean
  • KNN
  • Norm
  • None
include_text_features
boolean
default:"false"
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.
params
object
CatBoost configuration parameters. You can check the official documentation for more details about Catboost’s parameters here.
depth
integer
default:"6"
The maximum depth of the tree.Values must be in the following range:
2depth16
iterations
[integer, null]
default:"1000"
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:
1iterations < inf
one_hot_max_size
integer
default:"10"
Maximum cardinality of variables to be one-hot encoded. Use one-hot encoding for all categorical features with a number of different values less than or equal to this value. Other variables will be target-encoded. Note that one-hot encoding is faster than the alternatives, so decreasing this value makes it more likely slower methods will be used. See CatBoost details for further information.Values must be in the following range:
2one_hot_max_size < inf
max_ctr_complexity
number
default:"2"
The maximum number of features that can be combined when transforming categorical variables. 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:
1max_ctr_complexity4
l2_leaf_reg
number
default:"3.0"
Coefficient at the L2 regularization term of the cost function.Values must be in the following range:
0.0 < l2_leaf_reg < inf
border_count
integer
default:"254"
The number of splits for numerical features.Values must be in the following range:
1border_count65535
random_strength
number
default:"1.0"
The amount of randomness to use for scoring splits. Use this parameter to avoid overfitting the model. The value multiplies the variance of a random variable (with zero mean) that is added to the score used to select splits when a tree is grown.Values must be in the following range:
0 < random_strength < inf
nan_mode
string
default:"Min"
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:
  • Forbidden
  • Min
  • Max
boosting_type
string
default:"Plain"
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:
  • Ordered
  • Plain
rsm
[number, null]
Random subspace method. The percentage of features to use at each split selection, when features are selected over again at random. The value null is equivalent to 1.0 (all features). You can set this to values < 1.0 when the dataset has many features (e.g. > 20) to speed up training.Values must be in the following range:
0 < rsm1.0
random_seed
integer
default:"0"
The random seed used for training.
used_ram_limit
[string, null]
Whether and how to limit memory usage. Select the maximum Ram used using strings like “2GB” or “100mb” (non case_sensitive).
auto_class_weights
[string, null]
default:"Balanced"
Whether and how to assign weights to different predicted classes. The options are:
  • null: No class weighting
  • Balanced: Inversely proportional to the number of samples/rows in each class
  • SqrtBalanced: Using the square root of the “Balanced” option.
Values must be one of the following:
  • Balanced
  • SqrtBalanced
  • None
  • None
validate
[object, null]
Configure model validation. Allows evaluation of model performance via cross-validation using custom metrics. If not specified, will by default perform 5-fold cross-validation with automatically selected metrics.
n_splits
[integer, null]
default:"5"
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.Values must be in the following range:
1n_splits < inf
test_size
[number, null]
What proportion of the data to use for testing in each split. If null or not provided, will use k-fold 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 < test_size < 1
time_split
boolean
default:"false"
Whether to split the data by time. Most recent data will be used for testing and previous data for training. Assumes data is passed already sorted ascending by time.
metrics
[array[string], null]
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.
Item
string
Each item in array.Values must be one of the following:accuracy balanced_accuracy f1_micro f1_macro f1_samples f1_weighted precision_micro precision_macro precision_samples precision_weighted recall_micro recall_macro recall_samples recall_weighted roc_auc roc_auc_ovr roc_auc_ovo roc_auc_ovr_weighted roc_auc_ovo_weighted
tune
object
Configure hypertuning. Configures the optimization of model hyper-parameters via cross-validated grid- or randomized search.
strategy
string
default:"grid"
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:
  • grid
  • random
iterations
integer
default:"10"
How many randomly sampled parameter combinations to test in randomized search.Values must be in the following range:
1 < iterations < inf
validate
[object, null]
Configure model validation. Allows evaluation of model performance via cross-validation using custom metrics. If not specified, will by default perform 5-fold cross-validation with automatically selected metrics.
n_splits
[integer, null]
default:"5"
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.Values must be in the following range:
1n_splits < inf
test_size
[number, null]
What proportion of the data to use for testing in each split. If null or not provided, will use k-fold 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 < test_size < 1
time_split
boolean
default:"false"
Whether to split the data by time. Most recent data will be used for testing and previous data for training. Assumes data is passed already sorted ascending by time.
metrics
[array[string], null]
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.
Item
string
Each item in array.Values must be one of the following:accuracy balanced_accuracy f1_micro f1_macro f1_samples f1_weighted precision_micro precision_macro precision_samples precision_weighted recall_micro recall_macro recall_samples recall_weighted roc_auc roc_auc_ovr roc_auc_ovo roc_auc_ovr_weighted roc_auc_ovo_weighted
scorer
string
Each item in array.Values must be one of the following:accuracy balanced_accuracy f1_micro f1_macro f1_samples f1_weighted precision_micro precision_macro precision_samples precision_weighted recall_micro recall_macro recall_samples recall_weighted roc_auc roc_auc_ovr roc_auc_ovo roc_auc_ovr_weighted roc_auc_ovo_weighted
params
object
The parameter values to explore. Allows tuning of any and all of the parameters that can be set also as constants in the “params” attribute.Keys in this object should be strings identifying parameter names, and values should be lists of values to explore for that parameter. E.g. "depth": [3, 5, 7].
depth
array[integer]
List of depths values to explore.
Item
integer
default:"6"
The maximum depth of the tree.Values must be in the following range:
2Item16
iterations
array[['integer', 'null']]
List of iterations values to explore.
Item
[integer, null]
default:"1000"
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:
1Item < inf
one_hot_max_size
array[integer]
List of values configuring max cardinality for one-hot encoding.
Item
integer
default:"10"
Maximum cardinality of variables to be one-hot encoded. Use one-hot encoding for all categorical features with a number of different values less than or equal to this value. Other variables will be target-encoded. Note that one-hot encoding is faster than the alternatives, so decreasing this value makes it more likely slower methods will be used. See CatBoost details for further information.Values must be in the following range:
2Item < inf
max_ctr_complexity
array[number]
List of values configuring variable combination complexity.
Item
number
default:"2"
The maximum number of features that can be combined when transforming categorical variables. 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:
1Item4
l2_leaf_reg
array[number]
List of leaf regularization strengths.
Item
number
default:"3.0"
Coefficient at the L2 regularization term of the cost function.Values must be in the following range:
0.0 < Item < inf
border_count
array[integer]
List of border counts.
Item
integer
default:"254"
The number of splits for numerical features.Values must be in the following range:
1Item65535
random_strength
array[number]
List of random strengths.
Item
number
default:"1.0"
The amount of randomness to use for scoring splits. Use this parameter to avoid overfitting the model. The value multiplies the variance of a random variable (with zero mean) that is added to the score used to select splits when a tree is grown.Values must be in the following range:
0 < Item < inf
criterion
array[string]
List of criterion values to explore.
Item
string
default:"gini"
Function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.Values must be one of the following:
  • gini
  • entropy
splitter
array[string]
List of splitter values to explore.
Item
string
default:"best"
Strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split.Values must be one of the following:
  • best
  • random
max_depth
array[['integer', 'null']]
List of max_depth values to explore.
Item
[integer, null]
default:"10"
Maximum depth of the tree. The maximum depth of the tree. If null, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.Values must be in the following range:
1 < Item < inf
min_samples_split
array[number]
List of min_samples_split values to explore.
Item
number
default:"2"
Minimum number of samples required to split an internal node. The minimum number of samples required to split an internal node. If int, then consider min_samples_split as the minimum count. If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
min_samples_leaf
array[number]
List of min_samples_leaf values to explore.
Item
number
default:"1"
Minimum number of samples required to be at a leaf node. The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.If int, then consider min_samples_leaf as the minimum count. If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.
max_leaf_nodes
array[['integer', 'null']]
List of max_leaf_nodes values to explore.
Item
[integer, null]
Grow a tree with max_leaf_nodes in best-first fashion. Best nodes are defined as relative reduction in impurity. If null then unlimited number of leaf nodes.
max_features
array[['number', 'string', 'null']]
List of max_features values to explore.
Item
[number, string, null]
Number of features to consider when looking for the best split. The number of features to consider when looking for the best split:
  • If int, then consider max_features features at each split.
  • If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
  • If “auto”, then max_features=sqrt(n_features).
  • If “sqrt”, then max_features=sqrt(n_features).
  • If “log2”, then max_features=log2(n_features).
  • If null, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.
ccp_alpha
array[number]
List of ccp_alpha values to explore.
Item
number
default:"0.0"
Complexity parameter used for Minimal Cost-Complexity Pruning. Minimal Cost-Complexity Pruning recursively finds the node with the “weakest link”. The weakest link is characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first. As alpha increases, more of the tree is pruned, which increases the total impurity of its leaves.Values must be in the following range:
0.0Item < inf
seed
integer
Seed for random number generator ensuring reproducibility.Values must be in the following range:
0seed < inf
sort
object
Sort the data before training. If the data is not already sorted by time, you can sort it here. This is useful when you want to split the data by time, for example to train on older data and test on newer data (see the time_split parameter in validation configurations). If the data is already sorted by time, you can ignore this parameter.
columns
[string, array[string]]
One or more column to sort by.
Item
string (ds.column)
Each item in array.
ascending
[boolean, array[boolean]]
default:"true"
Sort order. Whether to sort in ascending or descending order. If the single value true is provided, or no value is specified, all columns will be sorted in ascending order. If a single false is provided, all columns will be sorted in descending order. If an array of booleans is provided, each column will be sorted according to the corresponding boolean value.
Item
boolean
Each item in array.
I