Title: Precision
Slug: precision
Summary: How to evaluate a Python machine learning using precision.
Date: 2017-09-15 12:00
Category: Machine Learning
Tags: Model Evaluation
Authors: Chris Albon
In [1]:
# Load libraries
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
In [2]:
# Generate features matrix and target vector
X, y = make_classification(n_samples = 10000,
n_features = 3,
n_informative = 3,
n_redundant = 0,
n_classes = 2,
random_state = 1)
In [3]:
# Create logistic regression
logit = LogisticRegression()
In [4]:
# Cross-validate model using precision
cross_val_score(logit, X, y, scoring="precision")
Out[4]: