这篇文章主要介绍“怎么用JavaScript预测鸢尾花品种”，在日常操作中，相信很多人在怎么用JavaScript预测鸢尾花品种问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答”怎么用JavaScript预测鸢尾花品种”的疑惑有所帮助！接下来，请跟着小编一起来学习吧！
import pandas as pd
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
df = pd.read_csv(r"iris\YT-Django-Iris-App-3xj9B0qqps-master\iris.csv")
x = ['sepal_length','sepal_width','petal_length','petal_width']
X = df[x]
y = df['classification']
X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.2,random_state=1)
训练数据集合测试数据集的比例是8:2
model = SVC(gamma='auto')
model.fit(X_train,Y_train)
predictions = model.predict(X_test)
输入数据预测
iris = [1,1,1,1]
results = model.predict([iris])
print(results)
结果results是一个列表
print(accuracy_score(Y_test,predictions))
运行代码得到结果为 0.966666666667
pd.to_pickle(model,r"new_model.pickle")
如果需要用这个模型可以直接读入
model = pd.read_pickle(r"new_model.pickle")
完整代码
import pandas as pd
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
df = pd.read_csv(r"iris\YT-Django-Iris-App-3xj9B0qqps-master\iris.csv")
print(df.head())
x = ['sepal_length','sepal_width','petal_length','petal_width']
X = df[x]
y = df['classification']
X_train, X_test, Y_train, Y_test = train_test_split(X,y,test_size=0.2,random_state=1)
model = SVC(gamma='auto')
model.fit(X_train,Y_train)
predictions = model.predict(X_test)
print(accuracy_score(Y_test,predictions))
pd.to_pickle(model,r"new_model.pickle")
model = pd.read_pickle(r"new_model.pickle")
iris = [1,1,1,1]
results = model.predict([iris])
print(results)到此，关于“怎么用JavaScript预测鸢尾花品种”的学习就结束了，希望能够解决大家的疑惑。理论与实践的搭配能更好的帮助大家学习，快去试试吧！若想继续学习更多相关知识，请继续关注亿速云网站，小编会继续努力为大家带来更多实用的文章！
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