- Sort Score
- Result 10 results
- Languages All
- Labels All
Results 2381 - 2390 of 4,759 for * (3.06 sec)
-
cohen_kappa_score — scikit-learn 1.7.1 document...
= [ "negative" , "positive" , "negative" , "neutral" , "positive"..."positive" ] >>> y2 = [ "negative" , "positive" , "negative" , "neutral"...scikit-learn.org/stable/modules/generated/sklearn.metrics.cohen_kappa_score.html -
balanced_accuracy_score — scikit-learn 1.7.1 do...
= [ 0 , 1 , 0 , 0 , 1 , 0 ] >>> y_pred = [ 0 , 1 , 0 , 0 , 0 ,...Ong, C.S.; Stephan, K.E.; Buhmann, J.M. (2010). The balanced...scikit-learn.org/stable/modules/generated/sklearn.metrics.balanced_accuracy_score.html -
rand_score — scikit-learn 1.7.1 documentation
rand_score ([ 0 , 0 , 1 , 1 ], [ 1 , 1 , 0 , 0 ]) 1.0 Labelings...0 , 0 , 1 , 1 ]) 0.83 Gallery examples # Adjustment for chance...scikit-learn.org/stable/modules/generated/sklearn.metrics.rand_score.html -
root_mean_squared_log_error — scikit-learn 1.7....
y_true = [ 3 , 5 , 2.5 , 7 ] >>> y_pred = [ 2.5 , 5 , 4 , 8 ] >>>...root_mean_squared_log_error ( y_true , y_pred ) 0.199... On this page This...scikit-learn.org/stable/modules/generated/sklearn.metrics.root_mean_squared_log_error.html -
DecisionTreeRegressor — scikit-learn 1.7.1 docu...
min_impurity_decrease = 0.0 , ccp_alpha = 0.0 , monotonic_cst = None ) [source].... Parameters : criterion {“squared_error”, “friedman_mse”, “absolute_error”,...scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeRegressor.html -
MLPRegressor — scikit-learn 1.7.1 documentation
function, returns f(x) = 1 / (1 + exp(-x)). ‘tanh’, the hyperbolic tan...function, returns f(x) = max(0, x) solver {‘lbfgs’, ‘sgd’, ‘adam’},...scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPRegressor.html -
MeanShift — scikit-learn 1.7.1 documentation
= np . array ([[ 1 , 1 ], [ 2 , 1 ], [ 1 , 0 ], ... [ 4 , 7 ],...= 2 ) . fit ( X ) >>> clustering . labels_ array([1, 1, 1, 0,...scikit-learn.org/stable/modules/generated/sklearn.cluster.MeanShift.html -
FrozenEstimator — scikit-learn 1.7.1 documentation
array(...) fit ( X , y , * args , ** kwargs ) [source] # No-op....FrozenEstimator ( clf ) >>> frozen_clf . fit ( X , y ) # No-op Froze...scikit-learn.org/stable/modules/generated/sklearn.frozen.FrozenEstimator.html -
make_gaussian_quantiles — scikit-learn 1.7.1 do...
[np.int64(2), np.int64(0), np.int64(1), np.int64(0), np.int64(2)]...X . shape (100, 2) >>> y . shape (100,) >>> list ( y [: 5 ]) [np.int64(2),...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_gaussian_quantiles.html -
make_swiss_roll — scikit-learn 1.7.1 documentation
make_swiss_roll ( noise = 0.05 , random_state = 0 ) >>> X . shape (100, 3) >>>...sklearn.datasets. make_swiss_roll ( n_samples = 100 , * , noise = 0.0 , random_state...scikit-learn.org/stable/modules/generated/sklearn.datasets.make_swiss_roll.html