2/8 iris_data_analysis

下午看了下lynda关系型数据库的课，instructor很有激情但是废话太多了，中途弃。还是找点tutorial pdf来看吧orz。

晚上简单做了个鸢尾花数据分析的Jupyter notebook，用的模型是K Nearest Neighbor。

from sklearn.datasets import load_iris
from sklearn.cross_validation import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn import metrics
from sklearn.cross_validation import cross_val_score
import numpy as np

iris = datasets.load_iris()
X = iris.data[:, :4] 
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=4)#X=feature y=label

knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
print(metrics.accuracy_score(y_test, y_pred))

k_range = list(range(1, 31))
k_scores = []
for k in k_range:
    knn = KNeighborsClassifier(n_neighbors=k)
    scores = cross_val_score(knn, X, y, cv=10, scoring='accuracy')
    k_scores.append(scores.mean())
print(k_scores)

plt.plot(k_range, k_scores)
plt.xlabel('Value K for KNN')
plt.ylabel('Cross_val_score')

最后的plot长这样：

可见K=20的时候 cross validation 得分最高0.98且模型最简单。（K越大模型越简单）