Update app.py

app.py

@@ -69,6 +69,7 @@ data.target_val = data.load_npy_file('val_target_small.npy')
 
 st.header('**Step 4:**  Train models')
 st.subheader('Train constant prediction model')
+st.latex(r'''\hat{y}=E[y_{limit}]''')
 st.code('''const_model = data.target_train.mean(axis = 0)''',language='python')
 
 const_model = data.target_train.mean(axis = 0)
@@ -76,6 +77,10 @@ const_model = data.target_train.mean(axis = 0)
 
 
 st.subheader('Train multiple linear regression model')
+st.latex(r'''\beta=(X^{T}_{train} X_{train})^{-1} X^{T}_{train} y_{train}
+\hat{y}=X^{T}_{train} \beta
+where X_{train} and X_{input} correspond to the training data and the input data you would like to inference on, respectively. 
+X_{train} and X_{input} both have a column of ones concatenated to the feature space for the bias.''')
 st.text('adding bias unit')
 st.code('''X = data.input_train
 bias_vector = np.ones((X.shape[0], 1))