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# Scikit-Learn
To run the scikit-learn examples make sure you have installed the following library:
```bash
pip install -U scikit-learn
```
The metrics in `evaluate` can be easily integrated with an Scikit-Learn estimator or [pipeline](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html#sklearn.pipeline.Pipeline).
However, these metrics require that we generate the predictions from the model. The predictions and labels from the estimators can be passed to `evaluate` mertics to compute the required values.
```python
import numpy as np
np.random.seed(0)
import evaluate
from sklearn.compose import ColumnTransformer
from sklearn.datasets import fetch_openml
from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
```
Load data from https://www.openml.org/d/40945:
```python
X, y = fetch_openml("titanic", version=1, as_frame=True, return_X_y=True)
```
Alternatively X and y can be obtained directly from the frame attribute:
```python
X = titanic.frame.drop('survived', axis=1)
y = titanic.frame['survived']
```
We create the preprocessing pipelines for both numeric and categorical data. Note that pclass could either be treated as a categorical or numeric feature.
```python
numeric_features = ["age", "fare"]
numeric_transformer = Pipeline(
steps=[("imputer", SimpleImputer(strategy="median")), ("scaler", StandardScaler())]
)
categorical_features = ["embarked", "sex", "pclass"]
categorical_transformer = OneHotEncoder(handle_unknown="ignore")
preprocessor = ColumnTransformer(
transformers=[
("num", numeric_transformer, numeric_features),
("cat", categorical_transformer, categorical_features),
]
)
```
Append classifier to preprocessing pipeline. Now we have a full prediction pipeline.
```python
clf = Pipeline(
steps=[("preprocessor", preprocessor), ("classifier", LogisticRegression())]
)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
```
As `Evaluate` metrics use lists as inputs for references and predictions, we need to convert them to Python lists.
```python
# Evaluate metrics accept lists as inputs for values of references and predictions
y_test = y_test.tolist()
y_pred = y_pred.tolist()
# Accuracy
accuracy_metric = evaluate.load("accuracy")
accuracy = accuracy_metric.compute(references=y_test, predictions=y_pred)
print("Accuracy:", accuracy)
# Accuracy: 0.79
```
You can use any suitable `evaluate` metric with the estimators as long as they are compatible with the task and predictions.
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