Support Vector Regression

Support Vector Regression 
import pandas as pd
from sklearn.datasets import load_iris
iris = load_iris()
iris.feature_names
['sepal length (cm)',
 'sepal width (cm)',
 'petal length (cm)',
 'petal width (cm)']
iris.target_names
array(['setosa', 'versicolor', 'virginica'], dtype='<U10')
df = pd.DataFrame(iris.data,columns=iris.feature_names)
df.head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2 
df['target'] = iris.target
df.head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
0 5.1 3.5 1.4 0.2 0
1 4.9 3.0 1.4 0.2 0
2 4.7 3.2 1.3 0.2 0
3 4.6 3.1 1.5 0.2 0
4 5.0 3.6 1.4 0.2 0 
df[df.target==1].head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
50 7.0 3.2 4.7 1.4 1
51 6.4 3.2 4.5 1.5 1
52 6.9 3.1 4.9 1.5 1
53 5.5 2.3 4.0 1.3 1
54 6.5 2.8 4.6 1.5 1 
df[df.target==2].head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target
100 6.3 3.3 6.0 2.5 2
101 5.8 2.7 5.1 1.9 2
102 7.1 3.0 5.9 2.1 2
103 6.3 2.9 5.6 1.8 2
104 6.5 3.0 5.8 2.2 2 
df['flower_name'] =df.target.apply(lambda x: iris.target_names[x])
df.head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target flower_name
0 5.1 3.5 1.4 0.2 0 setosa
1 4.9 3.0 1.4 0.2 0 setosa
2 4.7 3.2 1.3 0.2 0 setosa
3 4.6 3.1 1.5 0.2 0 setosa
4 5.0 3.6 1.4 0.2 0 setosa 
df[45:55]
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) target flower_name
45 4.8 3.0 1.4 0.3 0 setosa
46 5.1 3.8 1.6 0.2 0 setosa
47 4.6 3.2 1.4 0.2 0 setosa
48 5.3 3.7 1.5 0.2 0 setosa
49 5.0 3.3 1.4 0.2 0 setosa
50 7.0 3.2 4.7 1.4 1 versicolor
51 6.4 3.2 4.5 1.5 1 versicolor
52 6.9 3.1 4.9 1.5 1 versicolor
53 5.5 2.3 4.0 1.3 1 versicolor
54 6.5 2.8 4.6 1.5 1 versicolor 
df0 = df[:50]
df1 = df[50:100]
df2 = df[100:]
import matplotlib.pyplot as plt

Sepal length vs Sepal Width (Setosa vs Versicolor)

plt.xlabel('Sepal Length')
plt.ylabel('Sepal Width')
plt.scatter(df0['sepal length (cm)'], df0['sepal width (cm)'],color="green",marker='+')
plt.scatter(df1['sepal length (cm)'], df1['sepal width (cm)'],color="blue",marker='.')

Petal length vs Pepal Width (Setosa vs Versicolor)

plt.xlabel('Petal Length')
plt.ylabel('Petal Width')
plt.scatter(df0['petal length (cm)'], df0['petal width (cm)'],color="green",marker='+')
plt.scatter(df1['petal length (cm)'], df1['petal width (cm)'],color="blue",marker='.')

Train Using Support Vector Machine (SVM)

from sklearn.model_selection import train_test_split
X = df.drop(['target','flower_name'], axis='columns')
y = df.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
len(X_train)
120
len(X_test)
30
from sklearn.svm import SVC
model = SVC()
model.fit(X_train, y_train)
SVC()
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model.score(X_test, y_test)
1.0
model.predict([[4.8,3.0,1.5,0.3]])
/home/benedict/mambaforge/envs/cfast/lib/python3.11/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but SVC was fitted with feature names
  warnings.warn(
array([0])

Tune parameters

1. Regularization (C)

model_C = SVC(C=1)
model_C.fit(X_train, y_train)
model_C.score(X_test, y_test)
1.0
model_C = SVC(C=10)
model_C.fit(X_train, y_train)
model_C.score(X_test, y_test)
0.9666666666666667

2. Gamma

model_g = SVC(gamma=10)
model_g.fit(X_train, y_train)
model_g.score(X_test, y_test)
0.9666666666666667

3. Kernel

model_linear_kernal = SVC(kernel='linear')
model_linear_kernal.fit(X_train, y_train)
SVC(kernel='linear')
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model_linear_kernal.score(X_test, y_test)
0.9666666666666667

Exercise

Train SVM classifier using sklearn digits dataset (i.e. from sklearn.datasets import load_digits) and then,

  1. Measure accuracy of your model using different kernels such as rbf and linear.
  2. Tune your model further using regularization and gamma parameters and try to come up with highest accurancy score
  3. Use 80% of samples as training data size
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