Question

我是机器学习的新手，我目前正在玩MNIST机器学习，使用RandomForestClassifier。

我使用sklearn和熊猫。我有一个训练CSV数据集。

import pandas as pd
import numpy as np
from sklearn import model_selection
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
from sklearn.linear_model import SGDClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

train = pd.read_csv("train.csv")
features = train.columns[1:]
X = train[features]
y = train['label']

user_train = pd.read_csv("input.csv")
user_features = user_train.columns[1:]
y_train = user_train[user_features]
user_y = user_train['label']


X_train, X_test, y_train, y_test = model_selection.train_test_split(X/255.,y,test_size=1,random_state=0)

clf_rf = RandomForestClassifier()
clf_rf.fit(X_train, y_train)
y_pred_rf = clf_rf.predict(X_test)
acc_rf = accuracy_score(y_test, y_pred_rf)

print("pred : ", y_pred_rf)
print("random forest accuracy: ",acc_rf)

我有当前的代码，效果很好。它需要训练集，分割并采用一个元素进行测试，并进行预测。

我现在想要的是使用来自输入的测试数据，我有一个名为＆＃34; input.csv＆＃34;的新csv，我想预测这个csv中的值。

如何用输入数据替换model_selection.train_test_split？我确信反应非常明显，我找不到任何东西。

Answer 1

代码的以下部分未使用

user_train = pd.read_csv("input.csv")
user_features = user_train.columns[1:]
y_train = user_train[user_features]
user_y = user_train['label']

如果input.csv具有与train.csv相同的结构，您可能需要：

训练分类器并在input.csv数据集的分割上进行测试:(请参考http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html以了解如何设置测试大小）

input_train = pd.read_csv("input.csv")    
input_features = user_train.columns[1:]    
input_data = user_train[input_features]    
input_labels = user_train['label']
data_train, data_test, labels_train, labels_test = model_selection.train_test_split(input_data/255.,input_labels,test_size=1,random_state=0)

clf_rf = RandomForestClassifier()
clf_rf.fit(data_train, labels_train)
labels_pred_rf = clf_rf.predict(data_test)
acc_rf = accuracy_score(labels_test, labels_pred_rf)

在整个input.csv文件中测试以前训练过的分类器

input_train = pd.read_csv("input.csv")    
input_features = user_train.columns[1:]    
input_data = user_train[input_features]    
input_labels = user_train['label']

labels_pred_rf = clf_rf.predict(input_data)
acc_rf = accuracy_score(input_labels, labels_pred_rf)

从csv获取熊猫系列

1 个答案: