A Minimal Example of Machine Learning (with scikit-learn)Минимальный пример кода машинного обучения (с помощью scikit-learn)
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
data = [{'humidity': 80, 'wind': 20, 'temp': 15, 'clouds': 90, 'raining?': 'yes'},
{'humidity': 40, 'wind': 5, 'temp': 25, 'clouds': 15, 'raining?': 'no'},
{'humidity': 20, 'wind': 30, 'temp': 35, 'clouds': 50, 'raining?': 'no'},
{'humidity': 90, 'wind': 3, 'temp': 18, 'clouds': 100, 'raining?': 'yes'},
{'humidity': 70, 'wind': 13, 'temp': 22, 'clouds': 75, 'raining?': 'no'},
{'humidity': 85, 'wind': 10, 'temp': 17, 'clouds': 90, 'raining?': 'yes'},
{'humidity': 90, 'wind': 20, 'temp': 20, 'clouds': 80, 'raining?': 'yes'},
{'humidity': 60, 'wind': 5, 'temp': 23, 'clouds': 30, 'raining?': 'no'},
{'humidity': 95, 'wind': 25, 'temp': 13, 'clouds': 100, 'raining?': 'yes'},
{'humidity': 70, 'wind': 2, 'temp': 30, 'clouds': 100, 'raining?': 'no'},
]
df = pd.DataFrame(data, columns=['humidity', 'wind', 'temp', 'clouds', 'raining?'])
print(df)
X, y = df.to_numpy()[:8, :4], df.to_numpy()[:8, 4]
model = RandomForestClassifier()
model.fit(X, y)
model.predict([[95, 25, 13, 100],[70, 2, 30, 100]]).reshape(1, -1)
@pythonl
