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README.md

@@ -1,7 +1,7 @@
 
 # Model description
 
-This model is a decision tree classifier trained to predict obesity levels based on demographic, lifestyle, and diet-related features. The dataset includes variables including age, height, weight, caloric food intake, physical activity, water consumption, smoking behavior, and transportation habits. The target label is the obesity category, which includes seven classes ranging from Insufficient_Weight to Obesity_Type_III. The decision tree originally had 12 layers, which was cut down (pruned) to improve interpretability and reduce overfitting.
+This model is a decision tree classifier trained to predict obesity levels based on demographic, lifestyle, and diet-related features. The dataset includes variables including age, height, weight, caloric food intake, physical activity, water consumption, smoking behavior, and transportation habits. The target label is the obesity category, which includes seven classes ranging from Insufficient_Weight to Obesity_Type_III. The original decision tree grew very large, so hyperparameter tuning and controlled depth were applied to improve interpretability and reduce overfitting.
 
 ## Intended uses & limitations
 
@@ -16,77 +16,77 @@ This model is a decision tree classifier trained to predict obesity levels based
 <details>
 <summary> Click to expand </summary>
 
-|      Hyperparameter      | Value |
-| :----------------------: | :---: |
-|        ccp_alpha         |  0.0  |
-|       class_weight       |  None |
-|        criterion         |  gini |
-|        max_depth         |   6   |
-|       max_features       |  None |
-|      max_leaf_nodes      |  None |
-|  min_impurity_decrease   |  0.0  |
-|     min_samples_leaf     |   1   |
-|    min_samples_split     |   2   |
-| min_weight_fraction_leaf |  0.0  |
-|      monotonic_cst       |  None |
-|       random_state       |  None |
-|         splitter         |  best |
+|      Hyperparameter      |  Value  |
+| :----------------------: | :-----: |
+|        ccp_alpha         |   0.0   |
+|       class_weight       |   None  |
+|        criterion         | entropy |
+|        max_depth         |    9    |
+|       max_features       |   None  |
+|      max_leaf_nodes      |   None  |
+|  min_impurity_decrease   |   0.0   |
+|     min_samples_leaf     |    3    |
+|    min_samples_split     |    10   |
+| min_weight_fraction_leaf |   0.0   |
+|      monotonic_cst       |   None  |
+|       random_state       |    17   |
+|         splitter         |   best  |
 
 </details>
 
 ### Model Plot
 
-<style>#sk-container-id-4 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
-}#sk-container-id-4 {color: var(--sklearn-color-text);
-}#sk-container-id-4 pre {padding: 0;
-}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;
-}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
-}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;
-}#sk-container-id-4 div.sk-text-repr-fallback {display: none;
+<style>#sk-container-id-3 {/* Definition of color scheme common for light and dark mode */--sklearn-color-text: black;--sklearn-color-line: gray;/* Definition of color scheme for unfitted estimators */--sklearn-color-unfitted-level-0: #fff5e6;--sklearn-color-unfitted-level-1: #f6e4d2;--sklearn-color-unfitted-level-2: #ffe0b3;--sklearn-color-unfitted-level-3: chocolate;/* Definition of color scheme for fitted estimators */--sklearn-color-fitted-level-0: #f0f8ff;--sklearn-color-fitted-level-1: #d4ebff;--sklearn-color-fitted-level-2: #b3dbfd;--sklearn-color-fitted-level-3: cornflowerblue;/* Specific color for light theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));--sklearn-color-icon: #696969;@media (prefers-color-scheme: dark) {/* Redefinition of color scheme for dark theme */--sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));--sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));--sklearn-color-icon: #878787;}
+}#sk-container-id-3 {color: var(--sklearn-color-text);
+}#sk-container-id-3 pre {padding: 0;
+}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;
+}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed var(--sklearn-color-line);margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: var(--sklearn-color-background);
+}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }`but bootstrap.min.css set `[hidden] { display: none !important; }`so we also need the `!important` here to be able to override thedefault hidden behavior on the sphinx rendered scikit-learn.org.See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;
+}#sk-container-id-3 div.sk-text-repr-fallback {display: none;
 }div.sk-parallel-item,
 div.sk-serial,
 div.sk-item {/* draw centered vertical line to link estimators */background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));background-size: 2px 100%;background-repeat: no-repeat;background-position: center center;
-}/* Parallel-specific style estimator block */#sk-container-id-4 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
-}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
-}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;
-}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
-}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
-}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;
-}/* Serial-specific style estimator block */#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
+}/* Parallel-specific style estimator block */#sk-container-id-3 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 2px solid var(--sklearn-color-text-on-default-background);flex-grow: 1;
+}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: var(--sklearn-color-background);position: relative;
+}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;
+}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;
+}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;
+}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;
+}/* Serial-specific style estimator block */#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: var(--sklearn-color-background);padding-right: 1em;padding-left: 1em;
 }/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
 clickable and can be expanded/collapsed.
 - Pipeline and ColumnTransformer use this feature and define the default style
 - Estimators will overwrite some part of the style using the `sk-estimator` class
-*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-4 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
+*//* Pipeline and ColumnTransformer style (default) */#sk-container-id-3 div.sk-toggleable {/* Default theme specific background. It is overwritten whether we have aspecific estimator or a Pipeline/ColumnTransformer */background-color: var(--sklearn-color-background);
 }/* Toggleable label */
-#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
-}#sk-container-id-4 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
-}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
-}/* Toggleable content - dropdown */#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-4 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
-}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-4 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
-}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
-}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
-}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
+#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.5em;box-sizing: border-box;text-align: center;
+}#sk-container-id-3 label.sk-toggleable__label-arrow:before {/* Arrow on the left of the label */content: "▸";float: left;margin-right: 0.25em;color: var(--sklearn-color-icon);
+}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: var(--sklearn-color-text);
+}/* Toggleable content - dropdown */#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;border-radius: 0.25em;color: var(--sklearn-color-text);/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-3 div.sk-toggleable__content.fitted pre {/* unfitted */background-color: var(--sklearn-color-fitted-level-0);
+}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {/* Expand drop-down */max-height: 200px;max-width: 100%;overflow: auto;
+}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";
+}/* Pipeline/ColumnTransformer-specific style */#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: var(--sklearn-color-fitted-level-2);
 }/* Estimator-specific style *//* Colorize estimator box */
-#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
-}#sk-container-id-4 div.sk-label label.sk-toggleable__label,
-#sk-container-id-4 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
+#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
+}#sk-container-id-3 div.sk-label label.sk-toggleable__label,
+#sk-container-id-3 div.sk-label label {/* The background is the default theme color */color: var(--sklearn-color-text-on-default-background);
 }/* On hover, darken the color of the background */
-#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
+#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {color: var(--sklearn-color-text);background-color: var(--sklearn-color-unfitted-level-2);
 }/* Label box, darken color on hover, fitted */
-#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
-}/* Estimator label */#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
-}#sk-container-id-4 div.sk-label-container {text-align: center;
+#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {color: var(--sklearn-color-text);background-color: var(--sklearn-color-fitted-level-2);
+}/* Estimator label */#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;
+}#sk-container-id-3 div.sk-label-container {text-align: center;
 }/* Estimator-specific */
-#sk-container-id-4 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
-}#sk-container-id-4 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
+#sk-container-id-3 div.sk-estimator {font-family: monospace;border: 1px dotted var(--sklearn-color-border-box);border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;/* unfitted */background-color: var(--sklearn-color-unfitted-level-0);
+}#sk-container-id-3 div.sk-estimator.fitted {/* fitted */background-color: var(--sklearn-color-fitted-level-0);
 }/* on hover */
-#sk-container-id-4 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
-}#sk-container-id-4 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
+#sk-container-id-3 div.sk-estimator:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-2);
+}#sk-container-id-3 div.sk-estimator.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-2);
 }/* Specification for estimator info (e.g. "i" and "?") *//* Common style for "i" and "?" */.sk-estimator-doc-link,
 a:link.sk-estimator-doc-link,
 a:visited.sk-estimator-doc-link {float: right;font-size: smaller;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1em;height: 1em;width: 1em;text-decoration: none !important;margin-left: 1ex;/* unfitted */border: var(--sklearn-color-unfitted-level-1) 1pt solid;color: var(--sklearn-color-unfitted-level-1);
@@ -106,13 +106,13 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
 .sk-estimator-doc-link span {display: none;z-index: 9999;position: relative;font-weight: normal;right: .2ex;padding: .5ex;margin: .5ex;width: min-content;min-width: 20ex;max-width: 50ex;color: var(--sklearn-color-text);box-shadow: 2pt 2pt 4pt #999;/* unfitted */background: var(--sklearn-color-unfitted-level-0);border: .5pt solid var(--sklearn-color-unfitted-level-3);
 }.sk-estimator-doc-link.fitted span {/* fitted */background: var(--sklearn-color-fitted-level-0);border: var(--sklearn-color-fitted-level-3);
 }.sk-estimator-doc-link:hover span {display: block;
-}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-4 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-}#sk-container-id-4 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
+}/* "?"-specific style due to the `<a>` HTML tag */#sk-container-id-3 a.estimator_doc_link {float: right;font-size: 1rem;line-height: 1em;font-family: monospace;background-color: var(--sklearn-color-background);border-radius: 1rem;height: 1rem;width: 1rem;text-decoration: none;/* unfitted */color: var(--sklearn-color-unfitted-level-1);border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+}#sk-container-id-3 a.estimator_doc_link.fitted {/* fitted */border: var(--sklearn-color-fitted-level-1) 1pt solid;color: var(--sklearn-color-fitted-level-1);
 }/* On hover */
-#sk-container-id-4 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
-}#sk-container-id-4 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
+#sk-container-id-3 a.estimator_doc_link:hover {/* unfitted */background-color: var(--sklearn-color-unfitted-level-3);color: var(--sklearn-color-background);text-decoration: none;
+}#sk-container-id-3 a.estimator_doc_link.fitted:hover {/* fitted */background-color: var(--sklearn-color-fitted-level-3);
 }
-</style><div id="sk-container-id-4" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier(max_depth=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" checked><label for="sk-estimator-id-4" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;DecisionTreeClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html">?<span>Documentation for DecisionTreeClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>DecisionTreeClassifier(max_depth=6)</pre></div> </div></div></div></div>
+</style><div id="sk-container-id-3" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=9, min_samples_leaf=3,min_samples_split=10, random_state=17)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" checked><label for="sk-estimator-id-5" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;DecisionTreeClassifier<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.tree.DecisionTreeClassifier.html">?<span>Documentation for DecisionTreeClassifier</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, max_depth=9, min_samples_leaf=3,min_samples_split=10, random_state=17)</pre></div> </div></div></div></div>
 
 ## Evaluation Results
 
@@ -126,7 +126,7 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
 
 ### Decision Tree
 
-![Confusion Matrix](decision_tree6.png)
+![Confusion Matrix](optimized_decision_tree.png)
 
 # How to Get Started with the Model
 
@@ -154,12 +154,12 @@ https://archive.ics.uci.edu/dataset/544/estimation+of+obesity+levels+based+on+ea
 
 # Intended uses & limitations
 
-The intended use for this model is educational use, possible tool for exploring health data, research, classification, and interpretability techniques. The model performs very well for higher-risk categories but is less accurate for lower-risk categories. The model is not intended for actual medical diagnosis or treatment decisions. Limitations include that normal-weight and neighboring overweight classes overlap, making them harder to classify. The data is also self-reported, which may lead to bias or inaccuracies and not ready to be used for real production.
+The intended use for this model is educational use, possible tool for exploring health data, research, classification, and interpretability techniques. The model performs very well for higher-risk categories but is less accurate for lower-risk or borderline weight categories. The model is not intended for actual medical diagnosis or treatment decisions. Limitations include that normal-weight and neighboring overweight classes overlap, making them harder to classify. The data is also self reported, which may lead to bias or inaccuracies and is not ready to be used for real production.
 
 # Training Procedure
 
-I trained a Decision Tree Classifier on the obesity dataset using 16 features related to demographics, behavior, and lifestyle. The data was split into 80% training and 20% testing, but was then split into 75% training and 25% testing for validation. The decision tree was reduced to help with overfitting. The target variable was the 7-class obesity category. Evaluation metrics included overall accuracy and fairness metrics.
+I trained a Decision Tree Classifier on the obesity dataset using 16 features related to demographics, behavior, and lifestyle. The data was split into 80% training and 20% testing, then hyperparameter tuning was performed to improve model performance and reduce overfitting. The target variable was the 7-class obesity category. Evaluation metrics included overall accuracy, confusion matrix, and classification report.
 
 # Evaluation Results
 
-The model achieves approximately 89% accuracy. Its performance is strongest on more distinct obesity categories and weaker on categories that are closer together. A more complex model could lead to higher accuracy, but it would be less interpretable and harder to present to medical professionals. I would trust this model as a decision-support tool, but not as the sole basis for medical diagnosis because it can make mistakes. This model is a supportive screening tool and would be beneficial to flag individuals who need to change their lifestyle habits.
+The optimized model achieves approximately 93% accuracy. Its performance is strongest on more distinct obesity categories and slightly weaker on categories that are closer together. An even more complex model could lead to higher accuracy, but it would be less interpretable and harder to present to medical professionals. I would trust this model as a decision-support tool, but not as the sole basis for medical diagnosis because it can still make mistakes. This model is a supportive screening tool and would be beneficial to flag individuals who may need lifestyle changes or further medical evaluation.

model.pkl

@@ -1,3 +1,3 @@
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