Added 3 sections, drafted out 2 sections

Author: xhlulu

doc/python/ml-regression.md

@@ -1,7 +1,91 @@
-# Regression
+---
+jupyter:
+  jupytext:
+    notebook_metadata_filter: all
+    text_representation:
+      extension: .md
+      format_name: markdown
+      format_version: '1.1'
+      jupytext_version: 1.1.1
+  kernelspec:
+    display_name: Python 3
+    language: python
+    name: python3
+  language_info:
+    codemirror_mode:
+      name: ipython
+      version: 3
+    file_extension: .py
+    mimetype: text/x-python
+    name: python
+    nbconvert_exporter: python
+    pygments_lexer: ipython3
+    version: 3.7.6
+  plotly:
+    description: Visualize regression in scikit-learn with Plotly
+    display_as: ai_ml
+    language: python
+    layout: base
+    name: ML Regression
+    order: 2
+    page_type: example_index
+    permalink: python/ml-regression/
+    thumbnail: thumbnail/knn-classification.png
+---
 
+## Basic linear regression
 
-### Visualizing kNN Regression
+This example shows how to train a simple linear regression from `sklearn` to predicts the tips servers will receive based on the value of the total bill (dataset is included in `px.data`).
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.linear_model import LinearRegression
+
+df = px.data.tips()
+X = df.total_bill.values.reshape(-1, 1)
+
+model = LinearRegression()
+model.fit(X, df.tip)
+
+x_range = np.linspace(X.min(), X.max(), 100)
+y_range = model.predict(x_range.reshape(-1, 1))
+
+fig = px.scatter(df, x='total_bill', y='tip', opacity=0.65)
+fig.add_traces(go.Scatter(x=x_range, y=y_range, name='Regression Fit'))
+fig.show()
+```
+
+## Model generalization on unseen data
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+
+df = px.data.tips()
+X = df.total_bill.values.reshape(-1, 1)
+X_train, X_test, y_train, y_test = train_test_split(X, df.tip, random_state=0)
+
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+x_range = np.linspace(X.min(), X.max(), 100)
+y_range = model.predict(x_range.reshape(-1, 1))
+
+
+fig = go.Figure([
+    go.Scatter(x=X_train.squeeze(), y=y_train, name='train', mode='markers'),
+    go.Scatter(x=X_test.squeeze(), y=y_test, name='test', mode='markers'),
+    go.Scatter(x=x_range, y=y_range, name='prediction')
+])
+fig.show()
+```
+
+## Comparing different kNN models parameters
 
 ```python
 import numpy as np
@@ -27,6 +111,75 @@ fig.add_traces(go.Scatter(x=x_range, y=y_dist, name='Weights: Distance'))
 fig.show()
 ```
 
+## 3D regression surface with `px.scatter_3d` and `go.Surface`
+
+```python
+import numpy as np
+import plotly.express as px
+import plotly.graph_objects as go
+from sklearn.neighbors import KNeighborsRegressor
+
+mesh_size = .02
+margin = 0
+
+df = px.data.iris()
+features = ["sepal_width", "sepal_length", "petal_width"]
+
+X = df[['sepal_width', 'sepal_length']]
+y = df['petal_width']
+
+# Condition the model on sepal width and length, predict the petal width
+knn = KNeighborsRegressor(10, weights='distance')
+knn.fit(X, y)
+
+# Create a mesh grid on which we will run our model
+x_min, x_max = X.sepal_width.min() - margin, X.sepal_width.max() + margin
+y_min, y_max = X.sepal_length.min() - margin, X.sepal_length.max() + margin
+xrange = np.arange(x_min, x_max, mesh_size)
+yrange = np.arange(y_min, y_max, mesh_size)
+xx, yy = np.meshgrid(xrange, yrange)
+
+# Run kNN
+pred = knn.predict(np.c_[xx.ravel(), yy.ravel()])
+pred = pred.reshape(xx.shape)
+
+# Generate the plot
+fig = px.scatter_3d(df, x='sepal_width', y='sepal_length', z='petal_width')
+fig.update_traces(marker=dict(size=5))
+fig.add_traces(go.Surface(x=xrange, y=yrange, z=pred, name='pred_surface'))
+fig.show()
+```
+
+## Label polynomial fits with latex
+
+```python
+
+```
+
+## Prediction Error Plots
+
+
+### Simple Prediction Error
+
+```python
+
+```
+
+### Augmented Prediction Error plot using `px`
+
+```python
+
+```
+
+### Grid Search Visualization using `px.scatter_matrix`
+
+
+## Residual Plots
+
+```python
+
+```
+
 ### Reference
 
 Learn more about `px` here: