手写数字识别

选择的是手写数字识别。

采用sklearn.ensemble.RandomForestClassifier

下面贴上代码

import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier

train_data = pd.read_csv('data/train.csv')
test_data = pd.read_csv('data/test.csv')
clean_data = pd.read_csv('data/sample_submission.csv')

from sklearn.model_selection import train_test_split
train, validate = train_test_split(train_data, test_size=0.2, random_state=0)

train_x = train.drop('label', axis=1)
train_y = train.label

test_x = validate.drop('label', axis=1)
test_y = validate.label

model = RandomForestClassifier(random_state=0, n_estimators=500)
model.fit(train_x, train_y)

from sklearn.metrics import accuracy_score
predict = model.predict(test_x)

accuracy_score(test_y, predict)
prediction = model.predict(test_data)

submission = pd.DataFrame({'ImageId': [i for i in range(1, len(prediction) + 1)], 'Label': prediction})
submission.to_csv('submission.csv', index=False, header=True)

绘图：

plt.plot(range(len(test_y[:100])), test_y[:100], 'bo')
plt.plot(range(len(predict[:100])), predict[:100], 'r+')
plt.show()