seperated out the model metrics into its own notebook, and put functions in the model.py file

Author: DerikVo

Created_images/Neural Network confusion matrix.png

@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "id": "17b38552-4ffe-4ac9-b9ba-2ee96faa0e8d",
    "metadata": {
     "tags": []
@@ -142,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "48a01f31-0afb-4cc5-9de3-3f0b4e4a3eba",
    "metadata": {},
    "outputs": [],

Notebooks/02_Baseline Model.ipynb

@@ -1,4 +1,4 @@
 ,Accuracy,Precision,Recall,F1 Score
 baseline,0.4698703279938978,0.4683084749604378,0.4698703279938978,0.4635669509975083
-Neural Network,0.8520213577421816,0.850317486655664,0.8520213577421816,0.846736447406065
-Neural Network: regularization,0.8245614035087719,0.8200834793533461,0.8245614035087719,0.8186455106705496
+Neural Network: No regularization,0.8779557589626239,0.875790866125634,0.8779557589626239,0.8729339270233065
+Neural Network: With Regularization,0.8527841342486652,0.849915756851124,0.8527841342486652,0.8447039064139434

Notebooks/03_Neural_network.ipynb

@@ -6,7 +6,7 @@
 from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score
 import seaborn as sns
 import matplotlib.pyplot as plt
-
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
 
 '''
 This portion uses code from a previous project from this [notebook](https://github.com/DerikVo/DSI_project_4_plant_disease/blob/main/notebooks/01_Potato_PlantVillageEDA.ipynb).
@@ -184,3 +184,18 @@ def plot_confusion_matrix(confusion_matrix, class_paths, title):
     plt.savefig(f'../Created_images/{title} confusion matrix.png')
     #displays the image
     plt.show()
+
+    
+    
+def model_metrics(true_classes, predicted_classes, title):
+    '''
+    Calculate accuracy, precision, recall, and F1 score.
+    Also passes a title argument that titles the index for the model being used
+    '''
+    accuracy = accuracy_score(true_classes, predicted_classes)
+    precision = precision_score(true_classes, predicted_classes, average='weighted')
+    recall = recall_score(true_classes, predicted_classes, average='weighted')
+    f1 = f1_score(true_classes, predicted_classes, average='weighted')
+    data = {'Accuracy': [accuracy], 'Precision': [precision], 'Recall': [recall], 'F1 Score': [f1]}
+    df = pd.DataFrame(data, index=[f'{title}'])
+    return df