RandomForestClassifier_trained_pipeline_scores.html

<table border="1" class="dataframe">
  <tbody>
    <tr>
      <th>Problem</th>
      <td>Classification</td>
    </tr>
    <tr>
      <th>Target Column Name</th>
      <td>target</td>
    </tr>
    <tr>
      <th>Model's Name</th>
      <td>RandomForestClassifier</td>
    </tr>
    <tr>
      <th>Accuracy Score</th>
      <td>0.85000</td>
    </tr>
    <tr>
      <th>Roc Auc curve</th>
      <td>0.850</td>
    </tr>
    <tr>
      <th>Mean accuracy score of each tested hyperparameter combination</th>
      <td>0.732</td>
    </tr>
    <tr>
      <th>Range of all accuracy scores of each tested hyperparameter combination</th>
      <td>0.708 - 0.792</td>
    </tr>
    <tr>
      <th>Standard Deviation of scores</th>
      <td>0.031</td>
    </tr>
    <tr>
      <th>Standard Deviation &lt; 0.1 * Mean Accuracy scores</th>
      <td>The scores are relatively consistent.</td>
    </tr>
  </tbody>
</table><font size= 6><p><b> Classification Report:<br><table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>precision</th>
      <th>recall</th>
      <th>f1-score</th>
      <th>support</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>N</th>
      <td>0.838710</td>
      <td>0.866667</td>
      <td>0.852459</td>
      <td>30.00</td>
    </tr>
    <tr>
      <th>P</th>
      <td>0.862069</td>
      <td>0.833333</td>
      <td>0.847458</td>
      <td>30.00</td>
    </tr>
    <tr>
      <th>accuracy</th>
      <td>0.850000</td>
      <td>0.850000</td>
      <td>0.850000</td>
      <td>0.85</td>
    </tr>
    <tr>
      <th>macro avg</th>
      <td>0.850389</td>
      <td>0.850000</td>
      <td>0.849958</td>
      <td>60.00</td>
    </tr>
    <tr>
      <th>weighted avg</th>
      <td>0.850389</td>
      <td>0.850000</td>
      <td>0.849958</td>
      <td>60.00</td>
    </tr>
  </tbody>
</table><br><img src = "C:\Users\micha\Desktop\Proddis\new_experiment_data\normal_age_version\RandomForestClassifier_Pipeline\test_plot_classif_report.png" alt ="cfg"><br><font size= 6><b> Roc Auc curve figure:</b></font><br><img src = "C:\Users\micha\Desktop\Proddis\new_experiment_data\normal_age_version\RandomForestClassifier_Pipeline\plot_roc_curve.png" alt ="cfg"><br><font size= 6><p><b> Overfit Report:<br><table border="1" class="dataframe">
  <tbody>
    <tr>
      <th>Overfit Report</th>
      <td>The Report is based only on Accuracy</td>
    </tr>
    <tr>
      <th>Train set accuracy score of best pipeline</th>
      <td>0.8661</td>
    </tr>
    <tr>
      <th>Test set accuracy score of best pipeline</th>
      <td>0.8500</td>
    </tr>
    <tr>
      <th>Overfit estimation score of the best pipeline</th>
      <td>0.0161</td>
    </tr>
    <tr>
      <th>Learning Curve scores report</th>
      <td>The Learning Curve is based on Accuracy</td>
    </tr>
    <tr>
      <th>Train set accuracy score of learning curve's last value</th>
      <td>0.87</td>
    </tr>
    <tr>
      <th>Test set accuracy score of learning curve's last value</th>
      <td>0.78</td>
    </tr>
    <tr>
      <th>Overfit gap of learning curve's last value</th>
      <td>0.09</td>
    </tr>
  </tbody>
</table><br><font size= 6><b> Learning Curve - Overfitting or Underfitting:</b></font><br><img src = "C:\Users\micha\Desktop\Proddis\new_experiment_data\normal_age_version\RandomForestClassifier_Pipeline\overfitting_plot.png" alt ="cfg">