GB_CLASSIFY

Gradient boosting classification builds a sequence of shallow decision trees that iteratively correct earlier mistakes. At each stage m, a new weak learner h_m(x) is added to the model to minimize the loss function:

F_{m}(x) = F_{m-1}(x) + \gamma_m h_m(x)

This flexible nonlinear classifier is well-suited for tabular data and exposes feature-importance estimates based on how often features are used to split nodes across the ensemble.

This wrapper accepts rows as samples and a target supplied as a single row or single column. It returns training accuracy together with predicted labels, class counts, class probabilities, and fitted feature importances.

Excel Usage

=GB_CLASSIFY(data, target, n_estimators, learning_rate, max_depth, subsample, random_state)

data (list[list], required): 2D array of numeric feature data with rows as samples and columns as features.
target (list[list], required): Target labels as a single row, single column, or scalar when only one sample is present.
n_estimators (int, optional, default: 100): Number of boosting stages to fit.
learning_rate (float, optional, default: 0.1): Shrinkage factor applied to each boosting stage.
max_depth (int, optional, default: 3): Maximum depth of each individual regression tree.
subsample (float, optional, default: 1): Fraction of samples used to fit each boosting stage.
random_state (int, optional, default: null): Integer seed for reproducible boosting and tree construction. Leave blank for the estimator default.

Returns (dict): Excel data type containing training accuracy, predictions, probabilities, and fitted feature importances.

Example 1: Fit a gradient boosting classifier for two string-labeled groups

Inputs:

data		target	n_estimators	learning_rate	max_depth	subsample	random_state
0	0	cold	50	0.1	2	1	0
0	1	cold
1	0	cold
2	2	hot
2	3	hot
3	2	hot

Excel formula:

=GB_CLASSIFY({0,0;0,1;1,0;2,2;2,3;3,2}, {"cold";"cold";"cold";"hot";"hot";"hot"}, 50, 0.1, 2, 1, 0)

Expected output:

{"type":"Double","basicValue":1,"properties":{"accuracy":{"type":"Double","basicValue":1},"sample_count":{"type":"Double","basicValue":6},"feature_count":{"type":"Double","basicValue":2},"class_count":{"type":"Double","basicValue":2},"classes":{"type":"Array","elements":[[{"type":"String","basicValue":"cold"}],[{"type":"String","basicValue":"hot"}]]},"predictions":{"type":"Array","elements":[[{"type":"String","basicValue":"cold"}],[{"type":"String","basicValue":"cold"}],[{"type":"String","basicValue":"cold"}],[{"type":"String","basicValue":"hot"}],[{"type":"String","basicValue":"hot"}],[{"type":"String","basicValue":"hot"}]]},"prediction_counts":{"type":"Array","elements":[[{"type":"String","basicValue":"class"},{"type":"String","basicValue":"count"}],[{"type":"String","basicValue":"cold"},{"type":"Double","basicValue":3}],[{"type":"String","basicValue":"hot"},{"type":"Double","basicValue":3}]]},"probabilities":{"type":"Array","elements":[[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}]]},"feature_importances":{"type":"Array","elements":[[{"type":"Double","basicValue":0.293415}],[{"type":"Double","basicValue":0.706585}]]},"estimator_count":{"type":"Double","basicValue":50}}}

Example 2: Fit gradient boosting for one-dimensional numeric labels

Inputs:

data	target	n_estimators	learning_rate	max_depth	subsample	random_state
0	0	50	0.1	2	1	0
0.2	0
0.4	0
1.2	1
1.4	1
1.6	1

Excel formula:

=GB_CLASSIFY({0;0.2;0.4;1.2;1.4;1.6}, {0;0;0;1;1;1}, 50, 0.1, 2, 1, 0)

Expected output:

{"type":"Double","basicValue":1,"properties":{"accuracy":{"type":"Double","basicValue":1},"sample_count":{"type":"Double","basicValue":6},"feature_count":{"type":"Double","basicValue":1},"class_count":{"type":"Double","basicValue":2},"classes":{"type":"Array","elements":[[{"type":"Double","basicValue":0}],[{"type":"Double","basicValue":1}]]},"predictions":{"type":"Array","elements":[[{"type":"Double","basicValue":0}],[{"type":"Double","basicValue":0}],[{"type":"Double","basicValue":0}],[{"type":"Double","basicValue":1}],[{"type":"Double","basicValue":1}],[{"type":"Double","basicValue":1}]]},"prediction_counts":{"type":"Array","elements":[[{"type":"String","basicValue":"class"},{"type":"String","basicValue":"count"}],[{"type":"Double","basicValue":0},{"type":"Double","basicValue":3}],[{"type":"Double","basicValue":1},{"type":"Double","basicValue":3}]]},"probabilities":{"type":"Array","elements":[[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}]]},"feature_importances":{"type":"Array","elements":[[{"type":"Double","basicValue":1}]]},"estimator_count":{"type":"Double","basicValue":50}}}

Example 3: Fit a gradient boosting classifier for three separated groups

Inputs:

data		target	n_estimators	learning_rate	max_depth	subsample	random_state
0	0	left	50	0.1	2	1	0
0.2	0.1	left
4	4	center
4.2	3.9	center
8	0	right
8.2	0.1	right

Excel formula:

=GB_CLASSIFY({0,0;0.2,0.1;4,4;4.2,3.9;8,0;8.2,0.1}, {"left";"left";"center";"center";"right";"right"}, 50, 0.1, 2, 1, 0)

Expected output:

{"type":"Double","basicValue":1,"properties":{"accuracy":{"type":"Double","basicValue":1},"sample_count":{"type":"Double","basicValue":6},"feature_count":{"type":"Double","basicValue":2},"class_count":{"type":"Double","basicValue":3},"classes":{"type":"Array","elements":[[{"type":"String","basicValue":"center"}],[{"type":"String","basicValue":"left"}],[{"type":"String","basicValue":"right"}]]},"predictions":{"type":"Array","elements":[[{"type":"String","basicValue":"left"}],[{"type":"String","basicValue":"left"}],[{"type":"String","basicValue":"center"}],[{"type":"String","basicValue":"center"}],[{"type":"String","basicValue":"right"}],[{"type":"String","basicValue":"right"}]]},"prediction_counts":{"type":"Array","elements":[[{"type":"String","basicValue":"class"},{"type":"String","basicValue":"count"}],[{"type":"String","basicValue":"center"},{"type":"Double","basicValue":2}],[{"type":"String","basicValue":"left"},{"type":"Double","basicValue":2}],[{"type":"String","basicValue":"right"},{"type":"Double","basicValue":2}]]},"probabilities":{"type":"Array","elements":[[{"type":"Double","basicValue":0.000505015},{"type":"Double","basicValue":0.99899},{"type":"Double","basicValue":0.000505015}],[{"type":"Double","basicValue":0.000505015},{"type":"Double","basicValue":0.99899},{"type":"Double","basicValue":0.000505015}],[{"type":"Double","basicValue":0.99899},{"type":"Double","basicValue":0.000505015},{"type":"Double","basicValue":0.000505015}],[{"type":"Double","basicValue":0.99899},{"type":"Double","basicValue":0.000505015},{"type":"Double","basicValue":0.000505015}],[{"type":"Double","basicValue":0.000505015},{"type":"Double","basicValue":0.000505015},{"type":"Double","basicValue":0.99899}],[{"type":"Double","basicValue":0.000505015},{"type":"Double","basicValue":0.000505015},{"type":"Double","basicValue":0.99899}]]},"feature_importances":{"type":"Array","elements":[[{"type":"Double","basicValue":0.666667}],[{"type":"Double","basicValue":0.333333}]]},"estimator_count":{"type":"Double","basicValue":50}}}

Example 4: Flatten a single-row boolean target range for gradient boosting classification

Inputs:

data	target						n_estimators	learning_rate	max_depth	subsample	random_state
0	false	false	false	true	true	true	50	0.1	2	1	0
0.3
0.6
1.4
1.7
2

Excel formula:

=GB_CLASSIFY({0;0.3;0.6;1.4;1.7;2}, {FALSE,FALSE,FALSE,TRUE,TRUE,TRUE}, 50, 0.1, 2, 1, 0)

Expected output:

{"type":"Double","basicValue":1,"properties":{"accuracy":{"type":"Double","basicValue":1},"sample_count":{"type":"Double","basicValue":6},"feature_count":{"type":"Double","basicValue":1},"class_count":{"type":"Double","basicValue":2},"classes":{"type":"Array","elements":[[{"type":"Boolean","basicValue":false}],[{"type":"Boolean","basicValue":true}]]},"predictions":{"type":"Array","elements":[[{"type":"Boolean","basicValue":false}],[{"type":"Boolean","basicValue":false}],[{"type":"Boolean","basicValue":false}],[{"type":"Boolean","basicValue":true}],[{"type":"Boolean","basicValue":true}],[{"type":"Boolean","basicValue":true}]]},"prediction_counts":{"type":"Array","elements":[[{"type":"String","basicValue":"class"},{"type":"String","basicValue":"count"}],[{"type":"Boolean","basicValue":false},{"type":"Double","basicValue":3}],[{"type":"Boolean","basicValue":true},{"type":"Double","basicValue":3}]]},"probabilities":{"type":"Array","elements":[[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.996749},{"type":"Double","basicValue":0.00325104}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}],[{"type":"Double","basicValue":0.00325104},{"type":"Double","basicValue":0.996749}]]},"feature_importances":{"type":"Array","elements":[[{"type":"Double","basicValue":1}]]},"estimator_count":{"type":"Double","basicValue":50}}}

Python Code

import numpy as np
from sklearn.ensemble import GradientBoostingClassifier as SklearnGradientBoostingClassifier

def gb_classify(data, target, n_estimators=100, learning_rate=0.1, max_depth=3, subsample=1, random_state=None):
    """
    Fit a gradient boosting classifier and return training predictions.

    See: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html

    This example function is provided as-is without any representation of accuracy.

    Args:
        data (list[list]): 2D array of numeric feature data with rows as samples and columns as features.
        target (list[list]): Target labels as a single row, single column, or scalar when only one sample is present.
        n_estimators (int, optional): Number of boosting stages to fit. Default is 100.
        learning_rate (float, optional): Shrinkage factor applied to each boosting stage. Default is 0.1.
        max_depth (int, optional): Maximum depth of each individual regression tree. Default is 3.
        subsample (float, optional): Fraction of samples used to fit each boosting stage. Default is 1.
        random_state (int, optional): Integer seed for reproducible boosting and tree construction. Leave blank for the estimator default. Default is None.

    Returns:
        dict: Excel data type containing training accuracy, predictions, probabilities, and fitted feature importances.
    """
    def py(value):
        return value.item() if isinstance(value, np.generic) else value

    def cell(value):
        value = py(value)
        if isinstance(value, bool):
            return {"type": "Boolean", "basicValue": bool(value)}
        if isinstance(value, (int, float)) and not isinstance(value, bool):
            return {"type": "Double", "basicValue": float(value)}
        return {"type": "String", "basicValue": str(value)}

    def col(values):
        return [[cell(value)] for value in values]

    def mat(values):
        return [[cell(value) for value in row] for row in values]

    def parse_data(value):
        value = [[value]] if not isinstance(value, list) else value
        if not isinstance(value, list) or not value or not all(isinstance(row, list) and row for row in value):
            return None, "Error: data must be a non-empty 2D list"
        if len({len(row) for row in value}) != 1:
            return None, "Error: data must be a rectangular 2D list"
        data_np = np.array(value, dtype=float)
        if data_np.ndim != 2 or data_np.size == 0:
            return None, "Error: data must be a non-empty 2D list"
        if not np.isfinite(data_np).all():
            return None, "Error: data must contain only finite numeric values"
        return data_np, None

    def parse_target(value, sample_count):
        if not isinstance(value, list):
            labels = [value]
        elif not value:
            return None, "Error: target must be non-empty"
        elif all(not isinstance(item, list) for item in value):
            labels = value
        elif len(value) == 1:
            labels = value[0]
        elif all(isinstance(row, list) and len(row) == 1 for row in value):
            labels = [row[0] for row in value]
        else:
            return None, "Error: target must be a single row or column"

        if len(labels) != sample_count:
            return None, "Error: target length must match sample count"

        parsed = []
        classes = []
        for item in labels:
            item = py(item)
            if isinstance(item, str):
                if not item.strip():
                    return None, "Error: target labels must not be blank"
            elif isinstance(item, bool):
                item = bool(item)
            elif isinstance(item, (int, float)) and not isinstance(item, bool):
                if not np.isfinite(float(item)):
                    return None, "Error: target labels must be finite"
                item = float(item) if isinstance(item, float) else int(item)
            else:
                return None, "Error: target labels must be scalar string, boolean, or numeric values"
            parsed.append(item)
            if not any(type(existing) is type(item) and existing == item for existing in classes):
                classes.append(item)

        if len(classes) < 2:
            return None, "Error: target must contain at least 2 classes"
        return parsed, None

    def count_table(predictions, classes):
        rows = [[{"type": "String", "basicValue": "class"}, {"type": "String", "basicValue": "count"}]]
        for class_label in classes:
            count = sum(type(prediction) is type(class_label) and prediction == class_label for prediction in predictions)
            rows.append([cell(class_label), {"type": "Double", "basicValue": float(count)}])
        return rows

    try:
        data_np, error = parse_data(data)
        if error:
            return error

        target_values, error = parse_target(target, data_np.shape[0])
        if error:
            return error

        if int(n_estimators) < 1:
            return "Error: n_estimators must be at least 1"
        if float(learning_rate) <= 0:
            return "Error: learning_rate must be greater than 0"
        if int(max_depth) < 1:
            return "Error: max_depth must be at least 1"
        if float(subsample) <= 0 or float(subsample) > 1:
            return "Error: subsample must be greater than 0 and at most 1"

        fitted = SklearnGradientBoostingClassifier(
            n_estimators=int(n_estimators),
            learning_rate=float(learning_rate),
            max_depth=int(max_depth),
            subsample=float(subsample),
            random_state=None if random_state in (None, "") else int(random_state)
        ).fit(data_np, target_values)

        prediction_array = fitted.predict(data_np)
        predictions = [py(item) for item in prediction_array.tolist()]
        classes = [py(item) for item in fitted.classes_.tolist()]
        accuracy = float(np.mean([
            type(prediction) is type(actual) and prediction == actual
            for prediction, actual in zip(predictions, target_values)
        ]))

        return {
            "type": "Double",
            "basicValue": accuracy,
            "properties": {
                "accuracy": {"type": "Double", "basicValue": accuracy},
                "sample_count": {"type": "Double", "basicValue": float(data_np.shape[0])},
                "feature_count": {"type": "Double", "basicValue": float(data_np.shape[1])},
                "class_count": {"type": "Double", "basicValue": float(len(classes))},
                "classes": {"type": "Array", "elements": col(classes)},
                "predictions": {"type": "Array", "elements": col(predictions)},
                "prediction_counts": {"type": "Array", "elements": count_table(predictions, classes)},
                "probabilities": {"type": "Array", "elements": mat(fitted.predict_proba(data_np).tolist())},
                "feature_importances": {"type": "Array", "elements": col(fitted.feature_importances_.tolist())},
                "estimator_count": {"type": "Double", "basicValue": float(fitted.n_estimators_)}
            }
        }
    except Exception as e:
        return f"Error: {str(e)}"

Online Calculator

data *

2D array of numeric feature data with rows as samples and columns as features.

target *

Target labels as a single row, single column, or scalar when only one sample is present.

n_estimators

Number of boosting stages to fit.

learning_rate

Shrinkage factor applied to each boosting stage.

max_depth

Maximum depth of each individual regression tree.

subsample

Fraction of samples used to fit each boosting stage.

random_state

Integer seed for reproducible boosting and tree construction. Leave blank for the estimator default.