[Python][Scikit-learn][ML study notes 02] Iris classification prediction of logistic regression

To classify the iris data set, Logistic Regression is mainly used here .

The iris dataset is a 4-dimensional 3-class dataset, and its dataset is described as follows:

Data Set Characteristics:
:Number of Instances: 150 (50 in each of three classes)
:Number of Attributes: 4 numeric, predictive attributes and the class
:Attribute Information:
- sepal length in cm
- sepal width in cm
- petal length in cm
- petal width in cm
- class:
- Iris-Setosa
- Iris Versicolour
- Iris-Virginica

Test code:

# import dataset
iris = datasets.load_iris() # from sklearn import datasets
lg = linear_model.LogisticRegression(multi_class='ovr') # Multi-classification strategy using one-vs-rest
predicted = model_selection.cross_val_predict(lg, iris.data, iris.target, cv=5) # 5 KFold cross validation sets
# Determine the classification error rate
sums = 0
for i in range(len(predicted)):
    if predicted[i] == iris.target[i]:
        sums += 1
print sums * 100.0 / len(predicted), "%"
# cartography
fig, ax = plt.subplots() # import matplotlib.pyplot as plt
ax.plot(range(len(predicted)), predicted, 'gx', label='Predicted Class')
ax.plot(range(len(iris.target)), iris.target, 'r--', label='True Class')
plt.show()# http://blog.csdn.net/shenpibaipao

The classification accuracy can reach 96.0%