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Therefore with regards to this test data, the cumulative weight is >= 50% when y = 4. Therefore: Median = 4 For all the samples, we can get the total error by summing: Absolute(Median - y) * weight I.e., total error = (Absolute(4 - 3) * 0.1) + (Absolute(4 - 3) * 0.3) + (Absolute(4 - 4) * 1.0) + (Absolute(4 - 6) * 0.6) + (Absolute(4 - 7) * 0.3) = 2.5 Impurity = Total error / total weight = 2.5 / 2.3 = 1.08695652173913 ------------------ From this root node, the next best split is between X values of 3 and 5. 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The regression targeted by this test was that we were not reinitializing the criterion when it comes to the number of missing values. Therefore, the value of the critetion (i.e. MSE) was completely wrong. This test check that the MSE is null when there is a single sample in the leaf. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/28254 https://github.com/scikit-learn/scikit-learn/issues/28316 r>r8rIrrNrLrS) r]rqrrrrrvrrr flatnonzerorprt)r#r^rr_rZtree_refrv leaves_idxr|r|r}'test_regression_tree_missing_values_toyh s  "rRcCstj|}d}tj|tjddd}tj|ddddf<||t|}t|dd ||}|j j }t |dksJ|dS) Nrrr>r8ir:rkr) rqrCrrr;r]r3rrrrrvr)rrrXr^r_rrvr|r|r}-test_regression_extra_tree_missing_values_toy s   rSc Cstjdd\}}tjd}|}|jtjdtjd|dddgfdd  t }tj ||<t ||d d \}}}}tj gd tjd }tdddd} | ||||t| jjdksJt| jjdk| jjdk@} t| jj| ddS)aCheck that we properly handle missing values in clasification trees using a toy dataset. The test is more involved because we use a case where we detected a regression in a random forest. We therefore define the seed and bootstrap indices to detect one of the non-frequent regression. Here, we check that the impurity is null or positive in the leaves. Non-regression test for: https://github.com/scikit-learn/scikit-learn/issues/28254 T)Z return_X_yr)r8r7)rrNrLrK)nr r)prLQ'a[&.erUYRrrErVIJ3/kKnrZrh9rriO#MZrer[rU^rYrK]rtrcrpr1rUrdmrjrCrsrkra\4rZrurKrKraror1r1rXrrrVr\Nr1rvir>rrcr1fr{rUr\r8r`rH rirqjrrr8rorh>UrVrWPrb?rIrTr;r;Lrzrr;riHnrrr>r8rS)r load_irisrqrCrrr}rrrrLr3rrrrrrrvrPrprtr) r^r_rrJmaskrrrrrrQr|r|r}+test_classification_tree_missing_values_toy s,  "   rcCsLtddd}|tjtjt|j}t|j ||j }tj |j j tjd}d|d<t||j ||j j dksrJ|j dksJtt t|j j|jWdn1s0Yt|j jd|jdt|j ||j }tj |j j tjd}d|dd<t||j ||j j dks"J|j dks8J|j t|j j|jdS)zHTest pruning a tree with the Python caller of the Cythonized prune tree.rr8rkrr;N)rrr`rrrq atleast_1dr2rn_features_in_rrjrrmuint8rrrAssertionErrorr'rwrr$Z pruned_treeZleave_in_subtreer|r|r}test_build_pruned_tree_py s&   . rcCstddd}|tjtjt|j}t|j ||j }tj |j j tjd}d|d<tjtddt||j |Wdn1s0YdS)z8Test pruning a tree does not result in an infinite loop.rr8rkrz,Node has reached a leaf in the original treerN)rrr`rrrqrr2rrrrjrrmrrrrrrr|r|r}$test_build_pruned_tree_infinite_loop s  rcCs`tjd}|jddddtj}t|gd}td}t||dgd}t ||d S) zNon-regression test for gh-30554. 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