PLSRegression(n_components=2, *, scale=True, max_iter=500, tol=1e-06, copy=True) PLS 回归。 PLSRegression 也称为 PLS2 或 PLS1,具体取决于目标的数量。 在用户指南中阅读更多信息。 参数: n_components:整数,默认=2 要保留的组件数。应该在 [1, min(n_samples, n_features, n_targets)] 中...
用法:class sklearn.cross_decomposition.PLSRegression(n_components=2, *, scale=True, max_iter=500, tol=1e-06, copy=True) 参数: 该函数接受被上述和下面定义的五个参数: n_components:<int>:其默认值为2,它接受需要保留的组件数。 规模:<布尔>:其缺省值为True,并且它接受是否缩放数据或没有。 max_...
pls_not_scaled.fit(X,Y) Out[]: PLSRegression(copy=True, max_iter=500, n_components=2, scale=False, tol=1e-06) pls_scaled.fit(X,Y) Out[]: PLSRegression(copy=True, max_iter=500, n_components=2, scale=True, tol=1e-06) Out[]: PLSRegression(copy=True, max_iter=500, n_compon...
(y) # create PLSRegression model pls2 = PLSRegression(n_components=2) # split data x_train, x_test, y_train, y_test = train_test_split( df_x, df_y, test_size=0.30, random_state=1) # fit the model pls2.fit(x_train, y_train) # predict the values Y_pred = pls2.predict(...
Fixes #29662 What does this implement/fix? Explain your changes. The rank of X.T @ X is bounded above by min(n_samples, n_features) and not just n_features. For details see: #26204 (review) FIX Raises error in PLSRegression for invalid n_components Verified 5be4d28 github-actions...
5.2. Second analysis The modelling of N s 16 AA’s with the same linear model as before gives a substantially better result with A s 2 significant components and R 2 s 0.783, Q 2 s 0.706. The MLR model for these 16 objects gives a R 2 of 0.872, and a Q 2 of 0.608. This ...
N. Rutledge, 2004, Principle Components transform-partial least squares: a novel method to accelerate cross-validation in PLS regression. Chemometrics and Intelligent Laboratory Systems, 73 (2): 245-255.Barros, A.S.; Rutledge, D.N.; «Principal Components Transform - Partial Least Squares (...
Given the multicollinearity and high dimensionality of the data matrices generated with the MIA-QSAR approach, modeling techniques that involve the projection of the data space onto orthogonal components e.g. Partial Least Squares (PLS) have been generally used. However, the chemical interpretation of...
The significant PLS components are first obtained and the two predictor selection methods, called PLS–Forward and PLS–Bootstrap, are applied to the PLS model obtained. They are also compared empirically to two other methods that exist in the literature with respect to the quality of fit of ...
pls2 = cd.PLSRegression(n_components=3) pls2.fit(X, Y) expected = pls2.predict(X) self.assertIsInstance(result, pdml.ModelFrame) self.assert_numpy_array_almost_equal(result.values, expected) 开发者ID:pandas-ml,项目名称:pandas-ml,代码行数:23,代码来源:test_cross_decomposition.py ...