超簡単Pythonでアヤメ（Iris）分類（XGBoost 利用）機械学習

PythonでXGBoostを利用してアヤメ(Iris)分類を超簡単に機械学習

1. ツールインストール

＄ pip install install scikit-learn xgboost

2. ファイル作成

pred.py

from sklearn import datasets
from sklearn.model_selection import train_test_split
import xgboost as xgb
import numpy as np
from sklearn.metrics import precision_score


iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(
   X, y, test_size=0.20, random_state=42
)
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)
parameters = {
   "eta": 0.3,
   "objective": "multi:softprob",  # error evaluation for multiclass tasks
   "num_class": 3,  # number of classes to predic
   "max_depth": 3,  # depth of the trees in the boosting process
}
num_round = 20  # the number of training iterations
bst = xgb.train(parameters, dtrain, num_round)
preds = bst.predict(dtest)
best_preds = np.asarray([np.argmax(line) for line in preds])
print(precision_score(y_test, best_preds, average="macro"))
ax = xgb.plot_importance(bst)
ax.figure.savefig("fi.png")

3. 実行

$ python pred.py

1.0

fi.png

以上、超簡単！

4. 参考

XGBoost Documentation — xgboost 1.3.0-SNAPSHOT documentation