diff --git a/doc/pub/week45/html/._week45-bs001.html b/doc/pub/week45/html/._week45-bs001.html index 0fb9f6bc0..af30ca85d 100644 --- a/doc/pub/week45/html/._week45-bs001.html +++ b/doc/pub/week45/html/._week45-bs001.html @@ -173,7 +173,7 @@ MathJax.Hub.Config({
@@ -181,6 +181,8 @@ MathJax.Hub.Config({
  1. Video on Decision trees
  2. Video on boosting methods by Hastie.
    3. +
  3. Video on AdaBoost
    4. +
  4. Video on Gradient boost, part 1, parts 2-4 follows
@@ -190,7 +192,7 @@ MathJax.Hub.Config({
    -
  1. Add material about AdaBoost and Gradient boosting
    2. +
  2. Hastie et al, chapter 10.1-10.10
diff --git a/doc/pub/week45/html/._week45-bs002.html b/doc/pub/week45/html/._week45-bs002.html index 3462f8c93..bc9bc037f 100644 --- a/doc/pub/week45/html/._week45-bs002.html +++ b/doc/pub/week45/html/._week45-bs002.html @@ -178,25 +178,27 @@ MathJax.Hub.Config({
- 
# Common imports
+  
%matplotlib inline
+
+# Common imports
 from IPython.display import Image 
 from pydot import graph_from_dot_data
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.tree import DecisionTreeRegressor
 from sklearn.model_selection import train_test_split
 from sklearn.tree import export_graphviz
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.compose import ColumnTransformer
-from IPython.display import Image 
 from pydot import graph_from_dot_data
 from sklearn.datasets import load_breast_cancer
 from sklearn.svm import SVC
 from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import BaggingClassifier
-
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+import scikitplot as skplt
+from sklearn.preprocessing import StandardScaler
 import os
 
 # Where to save the figures and data files
@@ -229,37 +231,31 @@ X_train, X_test, y_train, y_test = train_tes
 print(X_train.shape)
 print(X_test.shape)
 #Scale the data
-from sklearn.preprocessing import StandardScaler
 scaler = StandardScaler()
 scaler.fit(X_train)
 X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 #define methods
 # Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
 logreg.fit(X_train_scaled, y_train)
 print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
-# Support Vector Machine
-svm.fit(X_train_scaled, y_train)
-print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
 # Decision Trees
+deep_tree_clf = DecisionTreeClassifier(max_depth=None)
 deep_tree_clf.fit(X_train_scaled, y_train)
 print("Test set accuracy with Decision Trees and scaled data: {:.2f}".format(deep_tree_clf.score(X_test_scaled,y_test)))
-
-
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import cross_validate
-# Data set not specificied
+# Support Vector Machine
+svm = SVC(gamma='auto', C=100)
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Random forests
 #Instantiate the model with 500 trees and entropy as splitting criteria
 Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
 Random_Forest_model.fit(X_train_scaled, y_train)
-#Cross validation
-accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
-print(accuracy)
 print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
 
 
-import scikitplot as skplt
+
 y_pred = Random_Forest_model.predict(X_test_scaled)
 skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
 plt.show()
diff --git a/doc/pub/week45/html/week45-reveal.html b/doc/pub/week45/html/week45-reveal.html
index 3f8253074..6cc7c9a20 100644
--- a/doc/pub/week45/html/week45-reveal.html
+++ b/doc/pub/week45/html/week45-reveal.html
@@ -199,7 +199,7 @@ MathJax.Hub.Config({
 
 
    
 
  •  Thursday: Boosting methods, froma AdaBoost to Gradient boosting
    • 
-
  •  Friday:  Gradient boosting and discussion of Decision trees and ensemble methods
    • 
+
  •  Friday:  Gradient boosting and discussion of Decision trees and ensemble methods. Wrapping up trees and start discussing Support Vector Machines
    • 
 

 

 

@@ -208,6 +208,8 @@ MathJax.Hub.Config({
 
    
 
  1.  Video on Decision trees
    2. 
 
  2.  Video on boosting methods by Hastie.
    3. 
+
  3.  Video on AdaBoost
    4. 
+
  4.  Video on Gradient boost, part 1, parts 2-4 follows
    5. 
 

 

 
@@ -216,7 +218,7 @@ MathJax.Hub.Config({
 Reading
 

 
    
-
  1.  Add material about AdaBoost and Gradient boosting
    2. 
+
  2.  Hastie et al, chapter 10.1-10.10
    3.
@@ -231,25 +233,27 @@ MathJax.Hub.Config({
- 
# Common imports
+  
%matplotlib inline
+
+# Common imports
 from IPython.display import Image 
 from pydot import graph_from_dot_data
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.tree import DecisionTreeRegressor
 from sklearn.model_selection import train_test_split
 from sklearn.tree import export_graphviz
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.compose import ColumnTransformer
-from IPython.display import Image 
 from pydot import graph_from_dot_data
 from sklearn.datasets import load_breast_cancer
 from sklearn.svm import SVC
 from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import BaggingClassifier
-
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+import scikitplot as skplt
+from sklearn.preprocessing import StandardScaler
 import os
 
 # Where to save the figures and data files
@@ -282,37 +286,31 @@ X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,ra
 print(X_train.shape)
 print(X_test.shape)
 #Scale the data
-from sklearn.preprocessing import StandardScaler
 scaler = StandardScaler()
 scaler.fit(X_train)
 X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 #define methods
 # Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
 logreg.fit(X_train_scaled, y_train)
 print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
-# Support Vector Machine
-svm.fit(X_train_scaled, y_train)
-print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
 # Decision Trees
+deep_tree_clf = DecisionTreeClassifier(max_depth=None)
 deep_tree_clf.fit(X_train_scaled, y_train)
 print("Test set accuracy with Decision Trees and scaled data: {:.2f}".format(deep_tree_clf.score(X_test_scaled,y_test)))
-
-
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import cross_validate
-# Data set not specificied
+# Support Vector Machine
+svm = SVC(gamma='auto', C=100)
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Random forests
 #Instantiate the model with 500 trees and entropy as splitting criteria
 Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
 Random_Forest_model.fit(X_train_scaled, y_train)
-#Cross validation
-accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
-print(accuracy)
 print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
 
 
-import scikitplot as skplt
+
 y_pred = Random_Forest_model.predict(X_test_scaled)
 skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
 plt.show()
diff --git a/doc/pub/week45/html/week45-solarized.html b/doc/pub/week45/html/week45-solarized.html
index 227923c39..55970f5e3 100644
--- a/doc/pub/week45/html/week45-solarized.html
+++ b/doc/pub/week45/html/week45-solarized.html
@@ -176,7 +176,7 @@ MathJax.Hub.Config({
 
 
    
 
  •  Thursday: Boosting methods, froma AdaBoost to Gradient boosting
    • 
-
  •  Friday:  Gradient boosting and discussion of Decision trees and ensemble methods
    • 
+
  •  Friday:  Gradient boosting and discussion of Decision trees and ensemble methods. Wrapping up trees and start discussing Support Vector Machines
    • 
 

 

 Videos
@@ -184,6 +184,8 @@ MathJax.Hub.Config({
 
    
 
  1.  Video on Decision trees
    2. 
 
  2.  Video on boosting methods by Hastie.
    3. 
+
  3.  Video on AdaBoost
    4. 
+
  4.  Video on Gradient boost, part 1, parts 2-4 follows
    5. 
 

 

 
@@ -192,7 +194,7 @@ MathJax.Hub.Config({
 Reading
 

 
    
-
  1.  Add material about AdaBoost and Gradient boosting
    2. 
+
  2.  Hastie et al, chapter 10.1-10.10
    3.
@@ -207,25 +209,27 @@ MathJax.Hub.Config({
- 
# Common imports
+  
%matplotlib inline
+
+# Common imports
 from IPython.display import Image 
 from pydot import graph_from_dot_data
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.tree import DecisionTreeRegressor
 from sklearn.model_selection import train_test_split
 from sklearn.tree import export_graphviz
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.compose import ColumnTransformer
-from IPython.display import Image 
 from pydot import graph_from_dot_data
 from sklearn.datasets import load_breast_cancer
 from sklearn.svm import SVC
 from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import BaggingClassifier
-
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+import scikitplot as skplt
+from sklearn.preprocessing import StandardScaler
 import os
 
 # Where to save the figures and data files
@@ -258,37 +262,31 @@ X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,ra
 print(X_train.shape)
 print(X_test.shape)
 #Scale the data
-from sklearn.preprocessing import StandardScaler
 scaler = StandardScaler()
 scaler.fit(X_train)
 X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 #define methods
 # Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
 logreg.fit(X_train_scaled, y_train)
 print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
-# Support Vector Machine
-svm.fit(X_train_scaled, y_train)
-print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
 # Decision Trees
+deep_tree_clf = DecisionTreeClassifier(max_depth=None)
 deep_tree_clf.fit(X_train_scaled, y_train)
 print("Test set accuracy with Decision Trees and scaled data: {:.2f}".format(deep_tree_clf.score(X_test_scaled,y_test)))
-
-
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import cross_validate
-# Data set not specificied
+# Support Vector Machine
+svm = SVC(gamma='auto', C=100)
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Random forests
 #Instantiate the model with 500 trees and entropy as splitting criteria
 Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
 Random_Forest_model.fit(X_train_scaled, y_train)
-#Cross validation
-accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
-print(accuracy)
 print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
 
 
-import scikitplot as skplt
+
 y_pred = Random_Forest_model.predict(X_test_scaled)
 skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
 plt.show()
diff --git a/doc/pub/week45/html/week45.html b/doc/pub/week45/html/week45.html
index cb9dfef72..3127dc949 100644
--- a/doc/pub/week45/html/week45.html
+++ b/doc/pub/week45/html/week45.html
@@ -253,7 +253,7 @@ MathJax.Hub.Config({
 
 
    
 
  •  Thursday: Boosting methods, froma AdaBoost to Gradient boosting
    • 
-
  •  Friday:  Gradient boosting and discussion of Decision trees and ensemble methods
    • 
+
  •  Friday:  Gradient boosting and discussion of Decision trees and ensemble methods. Wrapping up trees and start discussing Support Vector Machines
    • 
 

 

 Videos
@@ -261,6 +261,8 @@ MathJax.Hub.Config({
 
    
 
  1.  Video on Decision trees
    2. 
 
  2.  Video on boosting methods by Hastie.
    3. 
+
  3.  Video on AdaBoost
    4. 
+
  4.  Video on Gradient boost, part 1, parts 2-4 follows
    5. 
 

 

 
@@ -269,7 +271,7 @@ MathJax.Hub.Config({
 Reading
 

 
    
-
  1.  Add material about AdaBoost and Gradient boosting
    2. 
+
  2.  Hastie et al, chapter 10.1-10.10
    3.
@@ -284,25 +286,27 @@ MathJax.Hub.Config({
- 
# Common imports
+  
%matplotlib inline
+
+# Common imports
 from IPython.display import Image 
 from pydot import graph_from_dot_data
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.tree import DecisionTreeRegressor
 from sklearn.model_selection import train_test_split
 from sklearn.tree import export_graphviz
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.compose import ColumnTransformer
-from IPython.display import Image 
 from pydot import graph_from_dot_data
 from sklearn.datasets import load_breast_cancer
 from sklearn.svm import SVC
 from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import BaggingClassifier
-
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+import scikitplot as skplt
+from sklearn.preprocessing import StandardScaler
 import os
 
 # Where to save the figures and data files
@@ -335,37 +339,31 @@ X_train, X_test, y_train, y_test = train_tes
 print(X_train.shape)
 print(X_test.shape)
 #Scale the data
-from sklearn.preprocessing import StandardScaler
 scaler = StandardScaler()
 scaler.fit(X_train)
 X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 #define methods
 # Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
 logreg.fit(X_train_scaled, y_train)
 print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
-# Support Vector Machine
-svm.fit(X_train_scaled, y_train)
-print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
 # Decision Trees
+deep_tree_clf = DecisionTreeClassifier(max_depth=None)
 deep_tree_clf.fit(X_train_scaled, y_train)
 print("Test set accuracy with Decision Trees and scaled data: {:.2f}".format(deep_tree_clf.score(X_test_scaled,y_test)))
-
-
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import cross_validate
-# Data set not specificied
+# Support Vector Machine
+svm = SVC(gamma='auto', C=100)
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Random forests
 #Instantiate the model with 500 trees and entropy as splitting criteria
 Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
 Random_Forest_model.fit(X_train_scaled, y_train)
-#Cross validation
-accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
-print(accuracy)
 print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
 
 
-import scikitplot as skplt
+
 y_pred = Random_Forest_model.predict(X_test_scaled)
 skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
 plt.show()
diff --git a/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffiercgain.png b/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffiercgain.png
index 455369c77..ffba89938 100644
Binary files a/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffiercgain.png and b/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffiercgain.png differ
diff --git a/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffierconfusion.png b/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffierconfusion.png
index bd2dd5adf..4722b4d78 100644
Binary files a/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffierconfusion.png and b/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffierconfusion.png differ
diff --git a/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffierroc.png b/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffierroc.png
index 458ffab47..459a202f8 100644
Binary files a/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffierroc.png and b/doc/pub/week45/ipynb/Results/FigureFiles/gdclassiffierroc.png differ
diff --git a/doc/pub/week45/ipynb/Results/FigureFiles/gdregression.png b/doc/pub/week45/ipynb/Results/FigureFiles/gdregression.png
index 87fe1263d..1ed660285 100644
Binary files a/doc/pub/week45/ipynb/Results/FigureFiles/gdregression.png and b/doc/pub/week45/ipynb/Results/FigureFiles/gdregression.png differ
diff --git a/doc/pub/week45/ipynb/Results/FigureFiles/xdclassiffierconfusion.png b/doc/pub/week45/ipynb/Results/FigureFiles/xdclassiffierconfusion.png
index 3ab2f6024..7ce4eb554 100644
Binary files a/doc/pub/week45/ipynb/Results/FigureFiles/xdclassiffierconfusion.png and b/doc/pub/week45/ipynb/Results/FigureFiles/xdclassiffierconfusion.png differ
diff --git a/doc/pub/week45/ipynb/Results/FigureFiles/xdclassiffierroc.png b/doc/pub/week45/ipynb/Results/FigureFiles/xdclassiffierroc.png
index a9d37df15..9bcef7025 100644
Binary files a/doc/pub/week45/ipynb/Results/FigureFiles/xdclassiffierroc.png and b/doc/pub/week45/ipynb/Results/FigureFiles/xdclassiffierroc.png differ
diff --git a/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz b/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz
index 84265531f..5106caaf3 100644
Binary files a/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz and b/doc/pub/week45/ipynb/ipynb-week45-src.tar.gz differ
diff --git a/doc/pub/week45/ipynb/week45.ipynb b/doc/pub/week45/ipynb/week45.ipynb
index 6ea3e6a01..ec1733e0d 100644
--- a/doc/pub/week45/ipynb/week45.ipynb
+++ b/doc/pub/week45/ipynb/week45.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "06bf33a6",
+   "id": "5d0f5baa",
    "metadata": {
     "editable": true
    },
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1777bc2",
+   "id": "a1cc809f",
    "metadata": {
     "editable": true
    },
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f97929b1",
+   "id": "71a0b699",
    "metadata": {
     "editable": true
    },
@@ -38,7 +38,7 @@
     "\n",
     "* Thursday: Boosting methods, froma AdaBoost to Gradient boosting\n",
     "\n",
-    "* Friday:  Gradient boosting and discussion of Decision trees and ensemble methods\n",
+    "* Friday:  Gradient boosting and discussion of Decision trees and ensemble methods. Wrapping up trees and start discussing Support Vector Machines\n",
     "\n",
     "**Videos.**\n",
     "\n",
@@ -46,14 +46,18 @@
     "\n",
     "2. [Video on boosting methods by Hastie](https://www.youtube.com/watch?v=wPqtzj5VZus&ab_channel=H2O.ai).\n",
     "\n",
+    "3. [Video on AdaBoost](https://www.youtube.com/watch?v=LsK-xG1cLYA)\n",
+    "\n",
+    "4. [Video on Gradient boost, part 1, parts 2-4 follows](https://www.youtube.com/watch?v=3CC4N4z3GJc)\n",
+    "\n",
     "**Reading.**\n",
     "\n",
-    "1. Add material about AdaBoost and Gradient boosting"
+    "1. [Hastie et al, chapter 10.1-10.10](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/elementsstat.pdf)"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "da2cba17",
+   "id": "39e049b0",
    "metadata": {
     "editable": true
    },
@@ -64,13 +68,15 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "1054a91d",
+   "id": "ebd16dd3",
    "metadata": {
     "collapsed": false,
     "editable": true
    },
    "outputs": [],
    "source": [
+    "%matplotlib inline\n",
+    "\n",
     "%matplotlib inline\n",
     "\n",
     "# Common imports\n",
@@ -79,19 +85,19 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from sklearn.tree import DecisionTreeClassifier\n",
-    "from sklearn.tree import DecisionTreeRegressor\n",
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.tree import export_graphviz\n",
     "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
     "from sklearn.compose import ColumnTransformer\n",
-    "from IPython.display import Image \n",
     "from pydot import graph_from_dot_data\n",
     "from sklearn.datasets import load_breast_cancer\n",
     "from sklearn.svm import SVC\n",
     "from sklearn.linear_model import LogisticRegression\n",
-    "from sklearn.ensemble import BaggingClassifier\n",
-    "\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import cross_validate\n",
+    "import scikitplot as skplt\n",
+    "from sklearn.preprocessing import StandardScaler\n",
     "import os\n",
     "\n",
     "# Where to save the figures and data files\n",
@@ -124,37 +130,31 @@
     "print(X_train.shape)\n",
     "print(X_test.shape)\n",
     "#Scale the data\n",
-    "from sklearn.preprocessing import StandardScaler\n",
     "scaler = StandardScaler()\n",
     "scaler.fit(X_train)\n",
     "X_train_scaled = scaler.transform(X_train)\n",
     "X_test_scaled = scaler.transform(X_test)\n",
     "#define methods\n",
     "# Logistic Regression\n",
+    "logreg = LogisticRegression(solver='lbfgs')\n",
     "logreg.fit(X_train_scaled, y_train)\n",
     "print(\"Test set accuracy Logistic Regression with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
-    "# Support Vector Machine\n",
-    "svm.fit(X_train_scaled, y_train)\n",
-    "print(\"Test set accuracy SVM with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
     "# Decision Trees\n",
+    "deep_tree_clf = DecisionTreeClassifier(max_depth=None)\n",
     "deep_tree_clf.fit(X_train_scaled, y_train)\n",
     "print(\"Test set accuracy with Decision Trees and scaled data: {:.2f}\".format(deep_tree_clf.score(X_test_scaled,y_test)))\n",
-    "\n",
-    "\n",
-    "from sklearn.ensemble import RandomForestClassifier\n",
-    "from sklearn.preprocessing import LabelEncoder\n",
-    "from sklearn.model_selection import cross_validate\n",
-    "# Data set not specificied\n",
+    "# Support Vector Machine\n",
+    "svm = SVC(gamma='auto', C=100)\n",
+    "svm.fit(X_train_scaled, y_train)\n",
+    "print(\"Test set accuracy SVM with scaled data: {:.2f}\".format(logreg.score(X_test_scaled,y_test)))\n",
+    "# Random forests\n",
     "#Instantiate the model with 500 trees and entropy as splitting criteria\n",
     "Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion=\"entropy\")\n",
     "Random_Forest_model.fit(X_train_scaled, y_train)\n",
-    "#Cross validation\n",
-    "accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']\n",
-    "print(accuracy)\n",
     "print(\"Test set accuracy with Random Forests and scaled data: {:.2f}\".format(Random_Forest_model.score(X_test_scaled,y_test)))\n",
     "\n",
     "\n",
-    "import scikitplot as skplt\n",
+    "\n",
     "y_pred = Random_Forest_model.predict(X_test_scaled)\n",
     "skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)\n",
     "plt.show()\n",
@@ -167,7 +167,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b647351f",
+   "id": "d703c0d6",
    "metadata": {
     "editable": true
    },
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e7dbffcd",
+   "id": "ccee85a0",
    "metadata": {
     "editable": true
    },
@@ -201,7 +201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "494a82af",
+   "id": "a519ddc4",
    "metadata": {
     "editable": true
    },
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d27f68f8",
+   "id": "db6968e5",
    "metadata": {
     "editable": true
    },
@@ -230,7 +230,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4dabbeb4",
+   "id": "e7132763",
    "metadata": {
     "editable": true
    },
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7937173f",
+   "id": "647540de",
    "metadata": {
     "editable": true
    },
@@ -256,7 +256,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "8872158c",
+   "id": "fe9d52b3",
    "metadata": {
     "editable": true
    },
@@ -268,7 +268,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1e5b19b8",
+   "id": "00d09af0",
    "metadata": {
     "editable": true
    },
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c2e02d1",
+   "id": "722da425",
    "metadata": {
     "editable": true
    },
@@ -293,7 +293,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4244f14f",
+   "id": "3954663b",
    "metadata": {
     "editable": true
    },
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "91147e5f",
+   "id": "f63c1e38",
    "metadata": {
     "editable": true
    },
@@ -331,7 +331,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "af34f35f",
+   "id": "d8d5c7ac",
    "metadata": {
     "editable": true
    },
@@ -347,7 +347,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc2ea75e",
+   "id": "96cf5cd8",
    "metadata": {
     "editable": true
    },
@@ -359,7 +359,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5a5b4d62",
+   "id": "439579c9",
    "metadata": {
     "editable": true
    },
@@ -370,7 +370,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "daddba87",
+   "id": "c38ab9a3",
    "metadata": {
     "editable": true
    },
@@ -382,7 +382,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2a6e0f8d",
+   "id": "8e21cd6d",
    "metadata": {
     "editable": true
    },
@@ -392,7 +392,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f39ea127",
+   "id": "704b3592",
    "metadata": {
     "editable": true
    },
@@ -404,7 +404,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "62a2184d",
+   "id": "7476b8be",
    "metadata": {
     "editable": true
    },
@@ -414,7 +414,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a2781707",
+   "id": "03d12a19",
    "metadata": {
     "editable": true
    },
@@ -426,7 +426,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "95c004bf",
+   "id": "7fb4048b",
    "metadata": {
     "editable": true
    },
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4694fd52",
+   "id": "0c1adb18",
    "metadata": {
     "editable": true
    },
@@ -448,7 +448,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "06aea446",
+   "id": "60880562",
    "metadata": {
     "editable": true
    },
@@ -462,7 +462,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "20d085e6",
+   "id": "372bc35c",
    "metadata": {
     "editable": true
    },
@@ -478,7 +478,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "acc85c07",
+   "id": "b65e2b72",
    "metadata": {
     "editable": true
    },
@@ -490,7 +490,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "16f291d8",
+   "id": "12464e0a",
    "metadata": {
     "editable": true
    },
@@ -506,7 +506,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "c3a7df55",
+   "id": "efc71414",
    "metadata": {
     "editable": true
    },
@@ -518,7 +518,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9104a175",
+   "id": "bc2c815d",
    "metadata": {
     "editable": true
    },
@@ -528,7 +528,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "86ddaaa4",
+   "id": "de0906dc",
    "metadata": {
     "editable": true
    },
@@ -540,7 +540,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec36807d",
+   "id": "fc08da05",
    "metadata": {
     "editable": true
    },
@@ -552,7 +552,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f102ce80",
+   "id": "1ad3aec7",
    "metadata": {
     "editable": true
    },
@@ -564,7 +564,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d7b0991f",
+   "id": "4d661142",
    "metadata": {
     "editable": true
    },
@@ -575,7 +575,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "9c1752b5",
+   "id": "373fd6db",
    "metadata": {
     "editable": true
    },
@@ -587,7 +587,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7a9e3ab4",
+   "id": "241ade2a",
    "metadata": {
     "editable": true
    },
@@ -598,7 +598,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6fb6c431",
+   "id": "ced9d9f4",
    "metadata": {
     "editable": true
    },
@@ -610,7 +610,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "46ccbf78",
+   "id": "9b361f88",
    "metadata": {
     "editable": true
    },
@@ -620,7 +620,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cc303451",
+   "id": "e0665101",
    "metadata": {
     "editable": true
    },
@@ -632,7 +632,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b1c2be38",
+   "id": "a8189cf6",
    "metadata": {
     "editable": true
    },
@@ -644,7 +644,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e45307bf",
+   "id": "509020f9",
    "metadata": {
     "editable": true
    },
@@ -656,7 +656,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "edef26a5",
+   "id": "1ea5083e",
    "metadata": {
     "editable": true
    },
@@ -668,7 +668,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ef3f9795",
+   "id": "f021311a",
    "metadata": {
     "editable": true
    },
@@ -678,7 +678,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6772b2a5",
+   "id": "714b2b90",
    "metadata": {
     "editable": true
    },
@@ -690,7 +690,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "645ab1ac",
+   "id": "57df0a90",
    "metadata": {
     "editable": true
    },
@@ -700,7 +700,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "36afb932",
+   "id": "7dd87728",
    "metadata": {
     "editable": true
    },
@@ -712,7 +712,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a0e70734",
+   "id": "647ba1c0",
    "metadata": {
     "editable": true
    },
@@ -722,7 +722,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a20d1c75",
+   "id": "499ab775",
    "metadata": {
     "editable": true
    },
@@ -734,7 +734,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "415dcee2",
+   "id": "6fd9ba90",
    "metadata": {
     "editable": true
    },
@@ -744,7 +744,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "18c44408",
+   "id": "e2318f01",
    "metadata": {
     "editable": true
    },
@@ -756,7 +756,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "2bf44e31",
+   "id": "461b32c3",
    "metadata": {
     "editable": true
    },
@@ -766,7 +766,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3cfed10d",
+   "id": "c8b03689",
    "metadata": {
     "editable": true
    },
@@ -778,7 +778,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "84f4d3f8",
+   "id": "47d84c49",
    "metadata": {
     "editable": true
    },
@@ -798,7 +798,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4a886572",
+   "id": "16159393",
    "metadata": {
     "editable": true
    },
@@ -810,7 +810,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e2da363e",
+   "id": "297ad278",
    "metadata": {
     "editable": true
    },
@@ -820,7 +820,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "aec10b1a",
+   "id": "4c619128",
    "metadata": {
     "editable": true
    },
@@ -836,7 +836,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1814cd1d",
+   "id": "41cd0b50",
    "metadata": {
     "editable": true
    },
@@ -848,7 +848,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "72b3a813",
+   "id": "9ca95984",
    "metadata": {
     "editable": true
    },
@@ -876,7 +876,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7ec1c900",
+   "id": "1044743b",
    "metadata": {
     "editable": true
    },
@@ -889,7 +889,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "341c3079",
+   "id": "fcf973b1",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -921,7 +921,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ddaaaa66",
+   "id": "2ec8ee43",
    "metadata": {
     "editable": true
    },
@@ -939,7 +939,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "97b3c420",
+   "id": "740c50e2",
    "metadata": {
     "editable": true
    },
@@ -952,7 +952,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "3e0cffa8",
+   "id": "be50ed96",
    "metadata": {
     "editable": true
    },
@@ -964,7 +964,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "49e25f7c",
+   "id": "b247b123",
    "metadata": {
     "editable": true
    },
@@ -974,7 +974,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f4eda3ea",
+   "id": "8a250bd9",
    "metadata": {
     "editable": true
    },
@@ -986,7 +986,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6d9e5f3b",
+   "id": "01ec8e23",
    "metadata": {
     "editable": true
    },
@@ -996,7 +996,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f924c7ad",
+   "id": "43f1e9ca",
    "metadata": {
     "editable": true
    },
@@ -1008,7 +1008,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "78272ab5",
+   "id": "f097d76e",
    "metadata": {
     "editable": true
    },
@@ -1021,7 +1021,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0c5911f7",
+   "id": "c164dbbf",
    "metadata": {
     "editable": true
    },
@@ -1033,7 +1033,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "476e645a",
+   "id": "fa91537a",
    "metadata": {
     "editable": true
    },
@@ -1045,7 +1045,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f989a46",
+   "id": "1b7f5606",
    "metadata": {
     "editable": true
    },
@@ -1057,7 +1057,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b2668d41",
+   "id": "953f7aa6",
    "metadata": {
     "editable": true
    },
@@ -1067,7 +1067,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7f5965b6",
+   "id": "08b92a46",
    "metadata": {
     "editable": true
    },
@@ -1079,7 +1079,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "32e6877d",
+   "id": "36cf052c",
    "metadata": {
     "editable": true
    },
@@ -1089,7 +1089,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "61785a3c",
+   "id": "554af8ba",
    "metadata": {
     "editable": true
    },
@@ -1105,7 +1105,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "df29a69e",
+   "id": "ba8b8439",
    "metadata": {
     "editable": true
    },
@@ -1117,7 +1117,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d8b61f23",
+   "id": "8d9c4b52",
    "metadata": {
     "editable": true
    },
@@ -1138,7 +1138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "d067eeac",
+   "id": "597eaf22",
    "metadata": {
     "editable": true
    },
@@ -1149,7 +1149,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "2c80d8e8",
+   "id": "251b3392",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1201,7 +1201,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4128dc16",
+   "id": "bab59665",
    "metadata": {
     "editable": true
    },
@@ -1212,7 +1212,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "ecc2c3bf",
+   "id": "d1b0876c",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1263,7 +1263,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "7aa38b8c",
+   "id": "dd7e281f",
    "metadata": {
     "editable": true
    },
@@ -1286,7 +1286,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "6cf5f0de",
+   "id": "d2ac7037",
    "metadata": {
     "editable": true
    },
@@ -1297,7 +1297,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "3d356194",
+   "id": "fd8fdb2a",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -1349,7 +1349,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ed420a20",
+   "id": "ba15ae38",
    "metadata": {
     "editable": true
    },
@@ -1362,7 +1362,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "4de8cf9f",
+   "id": "f6f53545",
    "metadata": {
     "collapsed": false,
     "editable": true
diff --git a/doc/src/week45/week45.do.txt b/doc/src/week45/week45.do.txt
index 2c1c8182f..1de92a3d2 100644
--- a/doc/src/week45/week45.do.txt
+++ b/doc/src/week45/week45.do.txt
@@ -6,15 +6,17 @@ DATE: today
 ===== Overview of week 45 =====
 
 * Thursday: Boosting methods, froma AdaBoost to Gradient boosting
-* Friday:  Gradient boosting and discussion of Decision trees and ensemble methods
+* Friday:  Gradient boosting and discussion of Decision trees and ensemble methods. Wrapping up trees and start discussing Support Vector Machines
 
 !bblock Videos
 o "Video on Decision trees":"https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn"
 o "Video on boosting methods by Hastie":"https://www.youtube.com/watch?v=wPqtzj5VZus&ab_channel=H2O.ai".
+o "Video on AdaBoost":"https://www.youtube.com/watch?v=LsK-xG1cLYA"
+o "Video on Gradient boost, part 1, parts 2-4 follows":"https://www.youtube.com/watch?v=3CC4N4z3GJc"
 !eblock
 
 !bblock Reading  
-o Add material about AdaBoost and Gradient boosting
+o "Hastie et al, chapter 10.1-10.10":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/elementsstat.pdf"
 !eblock
 
 
@@ -22,25 +24,27 @@ o Add material about AdaBoost and Gradient boosting
 ===== Brief code reminder from last wekk =====
 
 !bc pycod
+%matplotlib inline
+
 # Common imports
 from IPython.display import Image 
 from pydot import graph_from_dot_data
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn.tree import DecisionTreeClassifier
-from sklearn.tree import DecisionTreeRegressor
 from sklearn.model_selection import train_test_split
 from sklearn.tree import export_graphviz
 from sklearn.preprocessing import StandardScaler, OneHotEncoder
 from sklearn.compose import ColumnTransformer
-from IPython.display import Image 
 from pydot import graph_from_dot_data
 from sklearn.datasets import load_breast_cancer
 from sklearn.svm import SVC
 from sklearn.linear_model import LogisticRegression
-from sklearn.ensemble import BaggingClassifier
-
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import cross_validate
+import scikitplot as skplt
+from sklearn.preprocessing import StandardScaler
 import os
 
 # Where to save the figures and data files
@@ -73,37 +77,31 @@ X_train, X_test, y_train, y_test = train_test_split(cancer.data,cancer.target,ra
 print(X_train.shape)
 print(X_test.shape)
 #Scale the data
-from sklearn.preprocessing import StandardScaler
 scaler = StandardScaler()
 scaler.fit(X_train)
 X_train_scaled = scaler.transform(X_train)
 X_test_scaled = scaler.transform(X_test)
 #define methods
 # Logistic Regression
+logreg = LogisticRegression(solver='lbfgs')
 logreg.fit(X_train_scaled, y_train)
 print("Test set accuracy Logistic Regression with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
-# Support Vector Machine
-svm.fit(X_train_scaled, y_train)
-print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
 # Decision Trees
+deep_tree_clf = DecisionTreeClassifier(max_depth=None)
 deep_tree_clf.fit(X_train_scaled, y_train)
 print("Test set accuracy with Decision Trees and scaled data: {:.2f}".format(deep_tree_clf.score(X_test_scaled,y_test)))
-
-
-from sklearn.ensemble import RandomForestClassifier
-from sklearn.preprocessing import LabelEncoder
-from sklearn.model_selection import cross_validate
-# Data set not specificied
+# Support Vector Machine
+svm = SVC(gamma='auto', C=100)
+svm.fit(X_train_scaled, y_train)
+print("Test set accuracy SVM with scaled data: {:.2f}".format(logreg.score(X_test_scaled,y_test)))
+# Random forests
 #Instantiate the model with 500 trees and entropy as splitting criteria
 Random_Forest_model = RandomForestClassifier(n_estimators=500,criterion="entropy")
 Random_Forest_model.fit(X_train_scaled, y_train)
-#Cross validation
-accuracy = cross_validate(Random_Forest_model,X_test_scaled,y_test,cv=10)['test_score']
-print(accuracy)
 print("Test set accuracy with Random Forests and scaled data: {:.2f}".format(Random_Forest_model.score(X_test_scaled,y_test)))
 
 
-import scikitplot as skplt
+
 y_pred = Random_Forest_model.predict(X_test_scaled)
 skplt.metrics.plot_confusion_matrix(y_test, y_pred, normalize=True)
 plt.show()
@@ -112,7 +110,6 @@ skplt.metrics.plot_roc(y_test, y_probas)
 plt.show()
 skplt.metrics.plot_cumulative_gain(y_test, y_probas)
 plt.show()
-
 !ec