怎么用JavaScript預(yù)測(cè)鳶尾花品種

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)

訓(xùn)練數(shù)據(jù)集合測(cè)試數(shù)據(jù)集的比例是8:2

model = SVC(gamma='auto')
model.fit(X_train,Y_train)
predictions = model.predict(X_test)

輸入數(shù)據(jù)預(yù)測(cè)

iris = [1,1,1,1]
results = model.predict([iris])
print(results)

結(jié)果results是一個(gè)列表

print(accuracy_score(Y_test,predictions))

運(yùn)行代碼得到結(jié)果為 0.966666666667

pd.to_pickle(model,r"new_model.pickle")

如果需要用這個(gè)模型可以直接讀入

model = pd.read_pickle(r"new_model.pickle")

完整代碼

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)