import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.multioutput import MultiOutputClassifier
# The data from your screenshot
# A B C D E F G
train_data = np.array([
[5, 133.5, 27, 284, 638, 31, 220],
[5, 111.9, 27, 285, 702, 36, 230],
[5, 99.3, 25, 310, 713, 39, 227],
[5, 102.5, 25, 311, 670, 34, 218],
[5, 114.8, 25, 312, 685, 34, 222],
])
# These I just made up
test_data_a = np.array([
[5, 100.0],
[5, 105.2],
[5, 102.7],
[5, 103.5],
[5, 120.3],
[5, 132.5],
[5, 152.5],
])
a = train_data[:, :2]
b = train_data[:, 2:]
forest = RandomForestClassifier(n_estimators=100, random_state=1)
classifier = MultiOutputClassifier(forest, n_jobs=-1)
classifier.fit(a, b)
print classifier.predict(test_data_a)
Output
[[ 25. 310. 713. 39. 227.]
[ 25. 311. 670. 34. 218.]
[ 25. 311. 670. 34. 218.]
[ 25. 311. 670. 34. 218.]
[ 25. 312. 685. 34. 222.]
[ 27. 284. 638. 31. 220.]
[ 27. 284. 638. 31. 220.]]