Report: Evaluation#
New in version 0.11.4.
We use different metrics to estimate a machine learning model’s performance, and to understand its strengths and weaknesses.
In this guide, we’ll show you how to easily generate a report with everything your need in one place using our evaluate_models
.
We’ll use the heart disease dataset, you can download it from here.
Download the data#
import urllib.request
import pandas as pd
url = (
"https://raw.githubusercontent.com/sharmaroshan/Heart-UCI-Dataset/master/heart.csv"
)
urllib.request.urlretrieve(
url,
filename="heart.csv",
)
data = pd.read_csv("heart.csv")
Prepare the data#
from sklearn.model_selection import train_test_split
column = "fbs"
X = data.drop(column, axis=1)
y = data[column]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=2023
)
Define the model#
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_score = model.predict_proba(X_test)
Evaluate the model#
from sklearn_evaluation.report import evaluate_model
report = evaluate_model(model, y_test, y_pred, y_score=y_score)
Embed the report#
report
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margin-bottom: 0;
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display: inline-block;
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text-transform: capitalize;
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display: none;
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margin-top: 10px;
max-width : 50%;
}
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<script>
function toggleErrorLogClick(id) {
el = document.getElementById(id)
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</head>
<body> <div class="model-evaluation-container"> <div> <h1>Model evaluation - RandomForestClassifier</h1>
<div class="block">
<ul>
<li class="nobull"><h2 class="capitalize">
balance</h2></li>
<li>Your test set is highly imbalanced</li>
<p class="display-inline-block">
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<li>If you need help understanding these stats, send us a message on <a href='https://ploomber.io/community'target='_blank'>slack</a></li>
</ul>
</div>
<div class="block">
<ul>
<li class="nobull"><h2 class="capitalize">
accuracy</h2></li>
<li>Accuracy is 0.9180327868852459</li>
<li>Please note your model is unbalanced, so high accuracy could be misleading</li>
</ul>
</div>
<div class="block">
<ul>
<li class="nobull"><h2 class="capitalize">
auc</h2></li>
<li>Area under curve is low for class 0</li>
<p class="display-inline-block">
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<li>If you need help understanding these stats, send us a message on <a href='https://ploomber.io/community'target='_blank'>slack</a></li>
<li>Number of classes : 1</li>
<li>AUC (roc) is : 0.4303571428571429</li>
</ul>
</div>
<div class="block">
<ul>
<li class="nobull"><h2 class="capitalize">
general stats</h2></li>
<p class="display-inline-block">
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</ul>
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