Placeholder Regression Section

Author: xhlulu

doc/python/ml-regression.md

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+# Regression
+
+
+### Visualizing kNN Regression
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.neighbors import KNeighborsRegressor
+
+df = px.data.tips()
+X = df.total_bill.values.reshape(-1, 1)
+
+knn_dist = KNeighborsRegressor(10, weights='distance')
+knn_uni = KNeighborsRegressor(10, weights='uniform')
+knn_dist.fit(X, df.tip)
+knn_uni.fit(X, df.tip)
+
+x_range = np.linspace(X.min(), X.max(), 100)
+y_dist = knn_dist.predict(x_range.reshape(-1, 1))
+y_uni = knn_uni.predict(x_range.reshape(-1, 1))
+
+fig = px.scatter(df, x='total_bill', y='tip', color='sex', opacity=0.65)
+fig.add_traces(go.Scatter(x=x_range, y=y_uni, name='Weights: Uniform'))
+fig.add_traces(go.Scatter(x=x_range, y=y_dist, name='Weights: Distance'))
+fig.show()
+```
+
+### Reference
+
+Learn more about `px` here:
+* https://plot.ly/python/plotly-express/
+
+This tutorial was inspired by amazing examples from the official scikit-learn docs:
+* https://scikit-learn.org/stable/auto_examples/neighbors/plot_regression.html