LeaveOneOut
交差検証(cross validation)の手法の一つで，テスト用に残す一つと，他のトレーニング用のindexを返す．
import numpy as npfrom sklearn.model_selection import LeaveOneOutX=np.random.randn(10,2)y=np.random.randn(10,1)loo = LeaveOneOut()for train_index, test_index in loo.split(X): print("TRAIN:", train_index, "TEST:", test_index)
結果は以下の通りTRAIN: [1 2 3 4 5 6 7 8 9] TEST: [0]TRAIN: [0 2 3 4 5 6 7 8 9] TEST: [1]TRAIN: [0 1 3 4 5 6 7 8 9] TEST: [2]TRAIN: [0 1 2 4 5 6 7 8 9] TEST: [3]TRAIN: [0 1 2 3 5 6 7 8 9] TEST: [4]TRAIN: [0 1 2 3 4 6 7 8 9] TEST: [5]TRAIN: [0 1 2 3 4 5 7 8 9] TEST: [6]TRAIN: [0 1 2 3 4 5 6 8 9] TEST: [7]TRAIN: [0 1 2 3 4 5 6 7 9] TEST: [8]TRAIN: [0 1 2 3 4 5 6 7 8] TEST: [9]
import numpy as npfrom sklearn.model_selection import LeaveOneOutX=np.random.randn(10,2)y=np.random.randn(10,1)loo = LeaveOneOut()for train_index, test_index in loo.split(X): print("TRAIN:", train_index, "TEST:", test_index)
結果は以下の通りTRAIN: [1 2 3 4 5 6 7 8 9] TEST: [0]TRAIN: [0 2 3 4 5 6 7 8 9] TEST: [1]TRAIN: [0 1 3 4 5 6 7 8 9] TEST: [2]TRAIN: [0 1 2 4 5 6 7 8 9] TEST: [3]TRAIN: [0 1 2 3 5 6 7 8 9] TEST: [4]TRAIN: [0 1 2 3 4 6 7 8 9] TEST: [5]TRAIN: [0 1 2 3 4 5 7 8 9] TEST: [6]TRAIN: [0 1 2 3 4 5 6 8 9] TEST: [7]TRAIN: [0 1 2 3 4 5 6 7 9] TEST: [8]TRAIN: [0 1 2 3 4 5 6 7 8] TEST: [9]