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The image you uploaded shows the function signature and description for a function used to split arrays or matrices into random train and test subsets. This function is likely train_test_split from the sklearn.model_selection module in the scikit-learn library. Here\u2019s a breakdown of the parameters and how to use them:<\/p>\n
Function Signature
\npython
\nCopy code
\n(*arrays: Any, test_size: Any | None = None, train_size: Any | None = None, random_state: Any | None = None, shuffle: bool = True, stratify: Any | None = None) -> list
\nDescription
\nPurpose: The function splits arrays or matrices into random train and test subsets.
\nUsage: It wraps input validation and application to input data into a single call for splitting (and optionally subsampling) data into a one-liner.
\nParameters
\n*arrays: Sequence of indexables with the same length\/shape[0]. This is where you pass the data you want to split. It can be features (X) and labels (y).
\ntest_size: The proportion of the dataset to include in the test split. Can be a float (e.g., 0.25 for 25% test data), an integer (number of samples), or None (in which case the value is set to the complement of the train size).
\ntrain_size: The proportion of the dataset to include in the train split. Can be a float, an integer, or None (in which case the value is automatically set to the complement of the test size).
\nrandom_state: Controls the shuffling applied to the data before splitting. Pass an integer for reproducible output across multiple function calls.
\nshuffle: Whether or not to shuffle the data before splitting. Default is True.
\nstratify: If not None, data is split in a stratified fashion, using this as the class labels.<\/p>\n
I understand that the concept can be a bit confusing, so let me clarify it further.<\/p>\n
The Here\u2019s a step-by-step explanation and demonstration:<\/p>\n What does Why does it matter?<\/strong><\/p>\n Different values for Let\u2019s demonstrate this with a simple example using As you can see, using If you run the above code multiple times with the same To verify this, you can run the split multiple times with the same This demonstrates that the split is consistent across multiple runs with the same \u4f7f\u7528colab\u8fdb\u884c\u3010\u4e2d\u6587\u5206\u8bcd\u6a21\u578b\u6d4b\u8bd5\u3011\uff0c\u7f16\u7a0b\u73af\u5883\u81ea\u52a8\u7ed9\u51fa\u4e86train_test_spint\u51fd\u6570\u7684\u63d0\u793a\uff0c\u6211\u5bf9\u4e8e\u8fd9\u79cd\u63d0\u793a\u4e0d\u592a\u6e05\u695a\uff0c\u5c31\u8ba9chatgpt\u6765\u89e3\u91ca\u3002 \u8bf7chatgpt\u7b2c\u4e00\u6b21\u89e3\u91ca The image you uploaded shows the function signature and description for a function used to split arrays or matrices into random train and test subsets. This function is likely train_test_split from the sklearn.model_selection module in the scikit-learn library. Here\u2019s a breakdown of the parameters and how to use them: Function Signature python Copy … Continue reading \u82f1\u6587\u662f\u6253\u5f00\u4e16\u754c\u7684\u94a5\u5319\uff0c\u540c\u5fd7\u5c1a\u9700\u52aa\u529b<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_bbp_topic_count":0,"_bbp_reply_count":0,"_bbp_total_topic_count":0,"_bbp_total_reply_count":0,"_bbp_voice_count":0,"_bbp_anonymous_reply_count":0,"_bbp_topic_count_hidden":0,"_bbp_reply_count_hidden":0,"_bbp_forum_subforum_count":0,"footnotes":""},"categories":[48,47],"tags":[],"class_list":["post-773","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","category-datascience"],"_links":{"self":[{"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/773","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=773"}],"version-history":[{"count":1,"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/773\/revisions"}],"predecessor-version":[{"id":774,"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/773\/revisions\/774"}],"wp:attachment":[{"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=773"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=773"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/madapapa.com\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=773"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}random_state<\/code> parameter in functions like train_test_split<\/code> controls the randomness of the operation. It ensures that the random processes involved (such as shuffling the data) are repeatable. By setting random_state<\/code> to a specific value, you ensure that the split will be the same each time you run the code, as long as the data and the code remain unchanged.<\/p>\n\n
random_state<\/code> do?<\/strong><\/p>\n\n
random_state<\/code> sets the seed for the random number generator. Different seeds will produce different sequences of random numbers, and therefore different shuffles and splits of the data.<\/li>\n<\/ul>\n<\/li>\n\n
random_state<\/code> value allows you to reproduce your results exactly. This is useful for debugging, sharing your results with others, and ensuring consistent results when rerunning experiments.<\/li>\n<\/ul>\n<\/li>\nrandom_state<\/code>:<\/strong><\/p>\n\n
random_state<\/code> values will initialize the random number generator differently, leading to different shuffles of the data and, consequently, different splits.<\/li>\n<\/ul>\n<\/li>\n<\/ol>\n<\/span><\/a>Demonstration<\/h3>\n
train_test_split<\/code> with different random_state<\/code> values.<\/p>\n<\/span><\/a>Using
random_state=1<\/code><\/h4>\nfrom sklearn.model_selection import train_test_split\n\n# Sample data\nX = [[1, 2], [3, 4], [5, 6], [7, 8]]\ny = [0, 1, 0, 1]\n\n# Split the data with random_state=1\nX_train_1, X_test_1, y_train_1, y_test_1 = train_test_split(X, y, test_size=0.25, random_state=1)\n\nprint("random_state=1")\nprint("X_train:", X_train_1)\nprint("X_test:", X_test_1)\nprint("y_train:", y_train_1)\nprint("y_test:", y_test_1)\n<\/code><\/pre>\n<\/span><\/a>Using
random_state=42<\/code><\/h4>\n# Split the data with random_state=42\nX_train_42, X_test_42, y_train_42, y_test_42 = train_test_split(X, y, test_size=0.25, random_state=42)\n\nprint("\\nrandom_state=42")\nprint("X_train:", X_train_42)\nprint("X_test:", X_test_42)\nprint("y_train:", y_train_42)\nprint("y_test:", y_test_42)\n<\/code><\/pre>\n<\/span><\/a>Expected Output<\/h3>\n
<\/span><\/a>For
random_state=1<\/code><\/h4>\nrandom_state=1\nX_train: [[5, 6], [1, 2], [7, 8]]\nX_test: [[3, 4]]\ny_train: [0, 0, 1]\ny_test: [1]\n<\/code><\/pre>\n<\/span><\/a>For
random_state=42<\/code><\/h4>\nrandom_state=42\nX_train: [[7, 8], [3, 4], [1, 2]]\nX_test: [[5, 6]]\ny_train: [1, 1, 0]\ny_test: [0]\n<\/code><\/pre>\nrandom_state=1<\/code> and random_state=42<\/code> results in different splits of the data. This is because the random number generator is initialized differently with each value, resulting in different shuffling of the data before it is split.<\/p>\n<\/span><\/a>Consistency Across Runs<\/h3>\n
random_state<\/code> value, you will get the same split every time. However, changing the random_state<\/code> value will change the split.<\/p>\n<\/span><\/a>Running Multiple Times for the Same
random_state<\/code><\/h3>\nrandom_state<\/code>:<\/p>\nfor i in range(3):\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n print(f"Run {i+1}:")\n print("X_train:", X_train)\n print("X_test:", X_test)\n print("y_train:", y_train)\n print("y_test:", y_test)\n print("-" * 30)\n<\/code><\/pre>\n<\/span><\/a>Expected Output for Each Run with
random_state=42<\/code><\/h3>\nRun 1:\nX_train: [[7, 8], [3, 4], [1, 2]]\nX_test: [[5, 6]]\ny_train: [1, 1, 0]\ny_test: [0]\n------------------------------\nRun 2:\nX_train: [[7, 8], [3, 4], [1, 2]]\nX_test: [[5, 6]]\ny_train: [1, 1, 0]\ny_test: [0]\n------------------------------\nRun 3:\nX_train: [[7, 8], [3, 4], [1, 2]]\nX_test: [[5, 6]]\ny_train: [1, 1, 0]\ny_test: [0]\n------------------------------\n<\/code><\/pre>\nrandom_state<\/code>. Changing the random_state<\/code> value will produce a different but consistent split for that specific value.<\/p>\n","protected":false},"excerpt":{"rendered":"