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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;
}

.model-evaluation-container h2 {
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}

.model-evaluation-container {
    font-family: Helvetica, sans-serif, Arial;
    text-align: left;
    width: fit-content;
    min-width: 100%;
    margin: 50px auto;
}

.model-evaluation-container .block {
    margin-bottom: 0px;
    border-bottom: 1px solid #e5e4e4;
    padding: 0.75em 0;
}

.model-evaluation-container .nobull {
    list-style-type: none;
}

.model-evaluation-container ul li {
    margin-bottom: 10px;
}

.model-evaluation-container ul {
    padding: 0;
}

.model-evaluation-container ul li:not(.nobull) {
    margin-left: 1em;
}

.model-evaluation-container .display-inline-block {
    display: inline-block;
}

.model-evaluation-container .capitalize {
    text-transform: capitalize;
}

.model-evaluation-container .hide {
    display: none;
}

.model-evaluation-container .error-log {
    margin-top: 10px;
    max-width : 50%;
}

    </style>

    <script>
        function toggleErrorLogClick(id) {
            el = document.getElementById(id)

            if (el.classList.contains("hide")) {
                el.classList.remove("hide")
            } else{
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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>




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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">
                            <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAoAAAAHgCAYAAAA10dzkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90

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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 : 0.48035714285714287</li>


                </ul>

            </div>

            <div class="block">

                <ul>
                    <li class="nobull"><h2 class="capitalize">
                    general stats</h2></li>



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