- Sort Score
- Num 10 results
- Language All
- Labels All
Results 1351 - 1360 of over 10,000 for 2 (1.25 seconds)
Filter
-
Comparing randomized search and grid search for...
243980072)} Model with rank: 2 Mean validation score: 0.983 (std:...222222222)} Model with rank: 2 Mean validation score: 0.998 (std:...scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html -
Manage deployments using the Elastic Cloud API ...
"region": "gcp-europe-west1"<2> } ' Optional: You can specify..."cluster_topology": [ { "zone_count": 2, "elasticsearch": { "node_attributes":...www.elastic.co/docs/deploy-manage/deploy/elastic-cloud/manage-deployments-using-elastic-cloud-api -
plot_release_highlights_1_8_0.ipynb
2, 2)\nx2, y2 = S_scaling.T\nax2.scatter(x2,...complexity compared to\n`O(n**2)` previously, which allows to...scikit-learn.org/stable/_downloads/826f1a05a1b6ad38fb98368f39e30aa6/plot_release_highlights_1_8_0... -
t-SNE: The effect of various perplexity values ...
2 sec circles, perplexity=50 in...n_samples = 150 n_components = 2 ( fig , subplots ) = plt . subplots...scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html -
AND 检索
如果想搜索包含”搜索词1”和”搜索词2”的文档,在搜索表单中输入如下内容: 搜索词1 AND 搜索词2 也可以用 AND 连接多个词。...fess.codelibs.org/zh-cn/15.5/user/search-and.html -
NOT 检索
如果想搜索包含”搜索词1”但不包含”搜索词2”的文档,输入并搜索如下内容: 搜索词1 NOT 搜索词2...fess.codelibs.org/zh-cn/15.5/user/search-not.html -
SVM: Weighted samples — scikit-learn 1.8.0 docu...
n_features = 2 , n_informative = 2 , n_redundant = 0 ,..., axes = plt . subplots ( 1 , 2 , figsize = ( 14 , 6 )) plot_decision_function...scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html -
IBM Instana - IBM Developer
of 2 pages 1 2 of 2 pages Previous page Next...developer.ibm.com/components/instana -
IBM Turbonomic - IBM Developer
of 2 pages 1 2 of 2 pages Previous page Next...developer.ibm.com/components/turbonomic -
PoissonRegressor — scikit-learn 1.8.0 documenta...
determination R^2. R^2 uses squared error and D^2 uses the deviance...PoissonRegressor () >>> X = [[ 1 , 2 ], [ 2 , 3 ], [ 3 , 4 ], [ 4 , 3...scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html