up slides

notebooks/1_introduction/N1_Linear_Classification.ipynb

@@ -101,21 +101,10 @@
 a = -model.coef_[0][0]/model.coef_[0][1]
 b = -model.intercept_[0]/model.coef_[0][1]
 print('boudary equation x_2={} x_1 + {}'.format(a,b))
 ```
 
-%%%% Output: error
-
-    ---------------------------------------------------------------------------
-    AttributeError                            Traceback (most recent call last)
-    <ipython-input-6-23b595ae4fed> in <module>
-          1 # What are the parameter values of the linear boundary equation x_2=a x_1 + b?
-    ----> 2 a = -model.scoef_[0][0]/model.coef_[0][1]
-          3 b = -model.intercept_[0]/model.coef_[0][1]
-          4 print('boudary equation x_2={} x_1 + {}'.format(a,b))
-    AttributeError: 'RidgeClassifier' object has no attribute 'scoef_'
-
 %% Cell type:markdown id: tags:
 
 ### Exercise
 Change the number of informative features from  `n_informative=2̀` to `n_informative=1` in the `make_classification()` procedure, regenerate the data set and fit the classification rule. Interpret now the new decision boundary: are the two variables of equal importance in predicting the class of the data?
 
@@ -124,10 +113,13 @@
 ``` python
 #get the documentation for sklearn RidgeClassification object
 linear_model.RidgeClassifier?
 ```
 
+%%%% Output: display_data
+
+
 %% Cell type:code id: tags:
 
 ``` python
 
 ```