compute_model_metrics.compute_model_metrics

compute_model_metrics.compute_model_metrics(model, X, y, metrics)

Compute evaluation metrics for a single fitted machine learning model.

Parameters

Name	Type	Description	Default
model	sklearn.base.BaseEstimator	A fitted scikit-learn model used for prediction.	required
X	array - like	Feature matrix for evaluation.	required
y	array - like	True target values.	required
metrics	dict	Dictionary where keys are metric names and values are callable metric functions from sklearn.metrics.	required

Returns

Name	Type	Description
	pandas.DataFrame	A dataframe containing one row of evaluation metrics corresponding to the input model.

Raises

Name	Type	Description
	ValueError	If the model is not fitted or if metric functions are invalid.

Examples

>>> import pandas as pd
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.metrics import accuracy_score
>>> from dsci524_group36_mlpipeline.compute_model_metrics import compute_model_metrics
>>>
>>> # Load a small example dataset
>>> df = pd.read_csv("https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv")
>>> X = df.drop(columns=["species"])
>>> y = df["species"]
>>>
>>> X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=123)
>>> model = LogisticRegression(max_iter=200).fit(X_train, y_train)
>>>
>>> metrics = {"accuracy": accuracy_score}
>>> compute_model_metrics(model, X_test, y_test, metrics)
   accuracy
0      ...