NB_Vs_SVM_Vs_Neural_Network

6.4774

0.34179

1369

-3.75030 -13.45860

17.5932 -2.77710

1370

-3.56370

-8.38270

12.3930 -1.28230

1371

-2.54190

-0.65804

2.6842

1.19520

1

1

1

1

1

We notice that:

We have 4 features: Variance, Skewness, Curtosis and Entropy;

We have 2 classes: Class 0 and Class 1;

We have at all 1372 samples.

https://colab.research.google.com/drive/1TGdz3ajSgpxDEy9lojFGKERc3bsiTaB4#printMode=true

1/5

02/11/2021 10:21

NB_Vs_SVM_Vs_Neural_Network.ipynb - Colaboratory

Partitioning Data

[X_train,X_test,y_train,y_test]=train_test_split(X,y,test_size=0.2) This function create two

parititions of the dataset with a test size of 0.2:

Train dataset (80% of the overall dataset)

Test dataset (20% of the overall dataset)

X denotes the matrix of features X-> delete from df the coloumn class

y denotes the label coloumn y-> troncate the df only on the coloumn class

1

2

3

X=df.drop('Class',axis=1)

y=df['Class']

X.head()

Variance Skewness Curtosis

Entropy

0

1

2

3

4

3.62160

8.6661

-2.8073 -0.44699

4.54590

8.1674

-2.4586 -1.46210

3.86600

-2.6383

1.9242

0.10645

3.45660

9.5228

-4.0112 -3.59440

0.32924

-4.4552

4.5718 -0.98880

1 [X_train,X_test,y_train,y_test]=train_test_split(X,y,test_size=0.2)

Train dataset = 80% Number of samples (1372) = 1372 0.8

Test dataset = 20% Number of samples (1372) = 1372 0.2

A.N: Train dataset = 1097.6 & Test dataset = 274.4

1

2

print("Train dataset size: {}/{}".format(len(X_train),len(y)))

print("Test dataset size: {}/{}".format(len(X_test),len(y)))

Train dataset size: 1097/1372

Test dataset size: 275/1372

X_train: Features of train;

y_train: Labels of X_train;

X_test : Fetaures of test;

y_test : Labels of X_test.

Machine Learning: NB Vs SVM Vs Neural Network

https://colab.research.google.com/drive/1TGdz3ajSgpxDEy9lojFGKERc3bsiTaB4#printMode=true

2/5

02/11/2021 10:21

NB_Vs_SVM_Vs_Neural_Network.ipynb - Colaboratory

We will compare between these 3 classiers on the same partitioned data. Let's start by the

initialization of the classier which we will compare.

1

2

3

4

5

6

gnb=GaussianNB() # gnb is a naive bayes classifier

linear_svm =SVC(kernel='linear') # linear_svm is a Linear Support Vectors

rbf_svm =SVC(kernel='rbf') # rbf_svm is a RBF support vectors

sigmoid_svm =SVC(kernel='sigmoid')# sigmoid support vectors

ploy_svm =SVC(kernel='poly',degree=2) # Ploynom with degree=2 as support vectors

neural=MLPClassifier(hidden_layer_sizes=(100,20),activation='relu',solver='adam') # neu

neural=MLPClassier parametres:

hidden_layer_sizes=(100,20): 4x100x20x2

activation='relu': activation function in all neurons is Relu(x)

solver='adam' : algorithm for weights' update during the training

defalut value of learning rate (alph): 0.001

Now, we will move to the training process with using of the t() function.

1

2

3

4

5

6

gnb.fit(X_train,y_train) # Train Guassian NB classifier

linear_svm.fit(X_train,y_train) # Train SVM

rbf_svm.fit(X_train,y_train)

sigmoid_svm.fit(X_train,y_train)

ploy_svm.fit(X_train,y_train)

neural.fit(X_train,y_train) # Train Neural Network - finding the best weight matrix

MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,

beta_2=0.999, early_stopping=False, epsilon=1e-08,

hidden_layer_sizes=(100, 20), learning_rate='constant',

learning_rate_init=0.001, max_fun=15000, max_iter=200,

momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,

power_t=0.5, random_state=None, shuffle=True, solver='adam',

tol=0.0001, validation_fraction=0.1, verbose=False,

warm_start=False)

Now, we will test the learned models!

We will ask the model to give a prediction based on its learning

Each Classier will produce a prediction; y_nb,y_linear_svm,etc.

We have two types of labels:

y_test: true label coming from the initial dataset

y_nb, y_linear_svm, y_rbf_svm, y_sigmoid_svm, y_ploy_svm et y_neural: are the labels

predicted by the models: naive bayes, svm with all kernels and neural network !!! Le modèle

est performant si et seulement si sa prédiction ègale aux vrais labels !!!

https://colab.research.google.com/drive/1TGdz3ajSgpxDEy9lojFGKERc3bsiTaB4#printMode=true

3/5

02/11/2021 10:21

NB_Vs_SVM_Vs_Neural_Network.ipynb - Colaboratory

1

2

3

4

5

6

y_nb=gnb.predict(X_test)

y_linear_svm=linear_svm.predict(X_test)

y_rbf_svm=rbf_svm.predict(X_test)

y_ploy_svm=ploy_svm.predict(X_test)

y_sigmoid_svm=sigmoid_svm.predict(X_test)

y_neural=neural.predict(X_test)

Performance Evaluation

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

print (' Peformance Evauation of Naive Bayes *')

print(confusion_matrix(y_test,y_nb))

print(classification_report(y_test,y_nb))

print (' Peformance Evauation of Linear SVM *')

print(confusion_matrix(y_test,y_linear_svm))

print(classification_report(y_test,y_linear_svm))

print (' Peformance Evauation of RBF SVM *')

print(confusion_matrix(y_test,y_rbf_svm))

print(classification_report(y_test,y_rbf_svm))

print (' Peformance Evauation of Sigmoid SVM *')

print(confusion_matrix(y_test,y_sigmoid_svm))

print(classification_report(y_test,y_sigmoid_svm))

print (' Peformance Evauation of Polynomial (2) SVM *')

print(confusion_matrix(y_test,y_ploy_svm))

print(classification_report(y_test,y_ploy_svm))

print (' Peformance Evauation of Neural Network *')

print(confusion_matrix(y_test,y_neural))

print(classification_report(y_test,y_neural))

Peformance Evauation of Naive Bayes *

[[129 17]

[ 29 100]]

precision recall f1-score support

0 0.82 0.88 0.85 146

1 0.85 0.78 0.81 129

accuracy 0.83 275

macro avg 0.84 0.83 0.83 275

weighted avg 0.83 0.83 0.83 275

Peformance Evauation of Linear SVM *

[[143 3]

[ 0 129]]

precision recall f1-score support

0 1.00 0.98 0.99 146

1 0.98 1.00 0.99 129

accuracy 0.99 275

macro avg 0.99 0.99 0.99 275

weighted avg 0.99 0.99 0.99 275

Peformance Evauation of RBF SVM *

[[144 2]

https://colab.research.google.com/drive/1TGdz3ajSgpxDEy9lojFGKERc3bsiTaB4#printMode=true

4/5

02/11/2021 10:21

NB_Vs_SVM_Vs_Neural_Network.ipynb - Colaboratory

[ 0 129]]

precision recall f1-score support

0 1.00 0.99 0.99 146

1 0.98 1.00 0.99 129

accuracy 0.99 275

macro avg 0.99 0.99 0.99 275

weighted avg 0.99 0.99 0.99 275

Peformance Evauation of Sigmoid SVM *

[[106 40]

[ 50 79]]

precision recall f1-score support

0 0.68 0.73 0.70 146

1 0.66 0.61 0.64 129

accuracy 0.67 275

macro avg 0.67 0.67 0.67 275

weighted avg 0.67 0.67 0.67 275

Peformance Evauation of Polynomial (2) SVM *

[[140 6]

[ 0 129]]

precision recall f1-score support

0 1.00 0.96 0.98 146

1 0.96 1.00 0.98 129

accuracy 0.98 275

macro avg 0.98 0.98 0.98 275

weighted avg 0.98 0.98 0.98 275

https://colab.research.google.com/drive/1TGdz3ajSgpxDEy9lojFGKERc3bsiTaB4#printMode=true

5/5