Update README.md

README.md

@@ -5,11 +5,9 @@ This model is a decision tree classifier trained to predict obesity levels based
 
 ## Intended uses & limitations
 
-[More Information Needed]
-
-## Training Procedure
-
-[More Information Needed]
+Intended use: Educational use, possible tool for exploring health data, research, and classification and interpretability techniques. The model performs very well for higher risk categories.
+Not intended for: Actual medical diagnosis or treatment decisions.
+Limitations: 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.
 
 ### Hyperparameters
 
@@ -115,8 +113,7 @@ div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
 </style><div id="sk-container-id-3" 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-3" type="checkbox" checked><label for="sk-estimator-id-3" 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>
 
 ## Evaluation Results
-
-[More Information Needed]
+The model achieves approximately 87% 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.
 
 # How to Get Started with the Model
 
@@ -130,29 +127,3 @@ predictions = model.predict(X)
 # Model Card Authors
 
 Kayleigh Carley
-
-# Model Card Contact
-
-You can contact the model card authors through following channels:
-[More Information Needed]
-
-# Citation
-
-Below you can find information related to citation.
-
-**BibTeX:**
-```
-[More Information Needed]
-```
-
-# Intended uses & limitations
-
-**Intended use:** Educational use, possible tool for exploring health data, research, and classification and interpretability techniques. The model performs very well for higher risk categories.
-
-**Not intended for:** Actual medical diagnosis or treatment decisions.
-
-**Limitations:** Normal-weight and neighboring overweight classes overlap, making them harder to classify. The data is also self-reported, whichmay lead to bias or inaccuracies.
-
-# Evaluation Results
-
-The model achieves approximately 87% 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.