{"flow":{"id":"19159","uploader":"32117","name":"sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.linear_model._stochastic_gradient.SGDClassifier)","custom_name":"sklearn.Pipeline(Pipeline,SGDClassifier)","class_name":"sklearn.pipeline.Pipeline","version":"1","external_version":"openml==0.12.2,sklearn==1.0.2","description":"Pipeline of transforms with a final estimator.\n\nSequentially apply a list of transforms and a final estimator.\nIntermediate steps of the pipeline must be 'transforms', that is, they\nmust implement `fit` and `transform` methods.\nThe final estimator only needs to implement `fit`.\nThe transformers in the pipeline can be cached using ``memory`` argument.\n\nThe purpose of the pipeline is to assemble several steps that can be\ncross-validated together while setting different parameters. For this, it\nenables setting parameters of the various steps using their names and the\nparameter name separated by a `'__'`, as in the example below. A step's\nestimator may be replaced entirely by setting the parameter with its name\nto another estimator, or a transformer removed by setting it to\n`'passthrough'` or `None`.","upload_date":"2022-09-25T22:43:28","language":"English","dependencies":"sklearn==1.0.2\nnumpy>=1.14.6\nscipy>=1.1.0\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"memory","data_type":"str or object with the joblib","default_value":"null","description":"Used to cache the fitted transformers of the pipeline. By default,\n no caching is performed. If a string is given, it is the path to\n the caching directory. Enabling caching triggers a clone of\n the transformers before fitting. Therefore, the transformer\n instance given to the pipeline cannot be inspected\n directly. Use the attribute ``named_steps`` or ``steps`` to\n inspect estimators within the pipeline. Caching the\n transformers is advantageous when fitting is time consuming"},{"name":"steps","data_type":"list of tuple","default_value":"[{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"numerical\", \"step_name\": \"numerical\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"model\", \"step_name\": \"model\"}}]","description":"List of (name, transform) tuples (implementing `fit`\/`transform`) that\n are chained, in the order in which they are chained, with the last\n object an estimator"},{"name":"verbose","data_type":"bool","default_value":"false","description":"If True, the time elapsed while fitting each step will be printed as it\n is completed."}],"component":[{"identifier":"numerical","flow":{"id":"19156","uploader":"32117","name":"sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)","custom_name":"sklearn.Pipeline(SimpleImputer,StandardScaler)","class_name":"sklearn.pipeline.Pipeline","version":"2","external_version":"openml==0.12.2,sklearn==1.0.2","description":"Pipeline of transforms with a final estimator.\n\nSequentially apply a list of transforms and a final estimator.\nIntermediate steps of the pipeline must be 'transforms', that is, they\nmust implement `fit` and `transform` methods.\nThe final estimator only needs to implement `fit`.\nThe transformers in the pipeline can be cached using ``memory`` argument.\n\nThe purpose of the pipeline is to assemble several steps that can be\ncross-validated together while setting different parameters. For this, it\nenables setting parameters of the various steps using their names and the\nparameter name separated by a `'__'`, as in the example below. A step's\nestimator may be replaced entirely by setting the parameter with its name\nto another estimator, or a transformer removed by setting it to\n`'passthrough'` or `None`.","upload_date":"2022-09-25T00:28:40","language":"English","dependencies":"sklearn==1.0.2\nnumpy>=1.14.6\nscipy>=1.1.0\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"memory","data_type":"str or object with the joblib","default_value":"null","description":"Used to cache the fitted transformers of the pipeline. By default,\n no caching is performed. If a string is given, it is the path to\n the caching directory. Enabling caching triggers a clone of\n the transformers before fitting. Therefore, the transformer\n instance given to the pipeline cannot be inspected\n directly. Use the attribute ``named_steps`` or ``steps`` to\n inspect estimators within the pipeline. Caching the\n transformers is advantageous when fitting is time consuming"},{"name":"steps","data_type":"list of tuple","default_value":"[{\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"Imputer\", \"step_name\": \"Imputer\"}}, {\"oml-python:serialized_object\": \"component_reference\", \"value\": {\"key\": \"scaler\", \"step_name\": \"scaler\"}}]","description":"List of (name, transform) tuples (implementing `fit`\/`transform`) that\n are chained, in the order in which they are chained, with the last\n object an estimator"},{"name":"verbose","data_type":"bool","default_value":"false","description":"If True, the time elapsed while fitting each step will be printed as it\n is completed."}],"component":[{"identifier":"scaler","flow":{"id":"19075","uploader":"29787","name":"sklearn.preprocessing._data.StandardScaler","custom_name":"sklearn.StandardScaler","class_name":"sklearn.preprocessing._data.StandardScaler","version":"11","external_version":"openml==0.12.2,sklearn==1.0.2","description":"Standardize features by removing the mean and scaling to unit variance.\n\nThe standard score of a sample `x` is calculated as:\n\n z = (x - u) \/ s\n\nwhere `u` is the mean of the training samples or zero if `with_mean=False`,\nand `s` is the standard deviation of the training samples or one if\n`with_std=False`.\n\nCentering and scaling happen independently on each feature by computing\nthe relevant statistics on the samples in the training set. Mean and\nstandard deviation are then stored to be used on later data using\n:meth:`transform`.\n\nStandardization of a dataset is a common requirement for many\nmachine learning estimators: they might behave badly if the\nindividual features do not more or less look like standard normally\ndistributed data (e.g. Gaussian with 0 mean and unit variance).\n\nFor instance many elements used in the objective function of\na learning algorithm (such as the RBF kernel of Support Vector\nMachines or the L1 and L2 regularizers of linear models) assume that\nall features are centered around 0 ...","upload_date":"2022-02-18T05:01:48","language":"English","dependencies":"sklearn==1.0.2\nnumpy>=1.14.6\nscipy>=1.1.0\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"copy","data_type":"bool","default_value":"true","description":"If False, try to avoid a copy and do inplace scaling instead\n This is not guaranteed to always work inplace; e.g. if the data is\n not a NumPy array or scipy.sparse CSR matrix, a copy may still be\n returned"},{"name":"with_mean","data_type":"bool","default_value":"true","description":"If True, center the data before scaling\n This does not work (and will raise an exception) when attempted on\n sparse matrices, because centering them entails building a dense\n matrix which in common use cases is likely to be too large to fit in\n memory"},{"name":"with_std","data_type":"bool","default_value":"true","description":"If True, scale the data to unit variance (or equivalently,\n unit standard deviation)."}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.0.2"]}},{"identifier":"Imputer","flow":{"id":"19084","uploader":"29930","name":"sklearn.impute._base.SimpleImputer","custom_name":"sklearn.SimpleImputer","class_name":"sklearn.impute._base.SimpleImputer","version":"30","external_version":"openml==0.12.2,sklearn==1.0.2","description":"Imputation transformer for completing missing values.","upload_date":"2022-03-08T11:40:33","language":"English","dependencies":"sklearn==1.0.2\nnumpy>=1.14.6\nscipy>=1.1.0\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"add_indicator","data_type":"bool","default_value":"false","description":"If True, a :class:`MissingIndicator` transform will stack onto output\n of the imputer's transform. This allows a predictive estimator\n to account for missingness despite imputation. If a feature has no\n missing values at fit\/train time, the feature won't appear on\n the missing indicator even if there are missing values at\n transform\/test time."},{"name":"copy","data_type":"bool","default_value":"true","description":"If True, a copy of `X` will be created. If False, imputation will\n be done in-place whenever possible. Note that, in the following cases,\n a new copy will always be made, even if `copy=False`:\n\n - If `X` is not an array of floating values;\n - If `X` is encoded as a CSR matrix;\n - If `add_indicator=True`"},{"name":"fill_value","data_type":"str or numerical value","default_value":"null","description":"When strategy == \"constant\", fill_value is used to replace all\n occurrences of missing_values\n If left to the default, fill_value will be 0 when imputing numerical\n data and \"missing_value\" for strings or object data types"},{"name":"missing_values","data_type":"int","default_value":"NaN","description":"The placeholder for the missing values. All occurrences of\n `missing_values` will be imputed. For pandas' dataframes with\n nullable integer dtypes with missing values, `missing_values`\n should be set to `np.nan`, since `pd.NA` will be converted to `np.nan`"},{"name":"strategy","data_type":"str","default_value":"\"mean\"","description":"The imputation strategy\n\n - If \"mean\", then replace missing values using the mean along\n each column. Can only be used with numeric data\n - If \"median\", then replace missing values using the median along\n each column. Can only be used with numeric data\n - If \"most_frequent\", then replace missing using the most frequent\n value along each column. Can be used with strings or numeric data\n If there is more than one such value, only the smallest is returned\n - If \"constant\", then replace missing values with fill_value. Can be\n used with strings or numeric data\n\n .. versionadded:: 0.20\n strategy=\"constant\" for fixed value imputation"},{"name":"verbose","data_type":"int","default_value":"0","description":"Controls the verbosity of the imputer"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.0.2"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.0.2"]}},{"identifier":"model","flow":{"id":"19160","uploader":"32117","name":"sklearn.linear_model._stochastic_gradient.SGDClassifier","custom_name":"sklearn.SGDClassifier","class_name":"sklearn.linear_model._stochastic_gradient.SGDClassifier","version":"3","external_version":"openml==0.12.2,sklearn==1.0.2","description":"Linear classifiers (SVM, logistic regression, etc.) with SGD training.\n\nThis estimator implements regularized linear models with stochastic\ngradient descent (SGD) learning: the gradient of the loss is estimated\neach sample at a time and the model is updated along the way with a\ndecreasing strength schedule (aka learning rate). SGD allows minibatch\n(online\/out-of-core) learning via the `partial_fit` method.\nFor best results using the default learning rate schedule, the data should\nhave zero mean and unit variance.\n\nThis implementation works with data represented as dense or sparse arrays\nof floating point values for the features. The model it fits can be\ncontrolled with the loss parameter; by default, it fits a linear support\nvector machine (SVM).\n\nThe regularizer is a penalty added to the loss function that shrinks model\nparameters towards the zero vector using either the squared euclidean norm\nL2 or the absolute norm L1 or a combination of both (Elastic Net). If the\nparameter update crosses the 0.0 value ...","upload_date":"2022-09-25T22:43:28","language":"English","dependencies":"sklearn==1.0.2\nnumpy>=1.14.6\nscipy>=1.1.0\njoblib>=0.11\nthreadpoolctl>=2.0.0","parameter":[{"name":"alpha","data_type":"float","default_value":"0.0001","description":"Constant that multiplies the regularization term. The higher the\n value, the stronger the regularization\n Also used to compute the learning rate when set to `learning_rate` is\n set to 'optimal'"},{"name":"average","data_type":"bool or int","default_value":"false","description":"When set to True, computes the averaged SGD weights across all\n updates and stores the result in the ``coef_`` attribute. If set to\n an int greater than 1, averaging will begin once the total number of\n samples seen reaches `average`. So ``average=10`` will begin\n averaging after seeing 10 samples."},{"name":"class_weight","data_type":"dict","default_value":"null","description":"Preset for the class_weight fit parameter\n\n Weights associated with classes. If not given, all classes\n are supposed to have weight one\n\n The \"balanced\" mode uses the values of y to automatically adjust\n weights inversely proportional to class frequencies in the input data\n as ``n_samples \/ (n_classes * np.bincount(y))``"},{"name":"early_stopping","data_type":"bool","default_value":"false","description":"Whether to use early stopping to terminate training when validation\n score is not improving. If set to True, it will automatically set aside\n a stratified fraction of training data as validation and terminate\n training when validation score returned by the `score` method is not\n improving by at least tol for n_iter_no_change consecutive epochs\n\n .. versionadded:: 0.20\n Added 'early_stopping' option"},{"name":"epsilon","data_type":"float","default_value":"0.1","description":"Epsilon in the epsilon-insensitive loss functions; only if `loss` is\n 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'\n For 'huber', determines the threshold at which it becomes less\n important to get the prediction exactly right\n For epsilon-insensitive, any differences between the current prediction\n and the correct label are ignored if they are less than this threshold"},{"name":"eta0","data_type":"float","default_value":"0.0","description":"The initial learning rate for the 'constant', 'invscaling' or\n 'adaptive' schedules. The default value is 0.0 as eta0 is not used by\n the default schedule 'optimal'"},{"name":"fit_intercept","data_type":"bool","default_value":"true","description":"Whether the intercept should be estimated or not. If False, the\n data is assumed to be already centered"},{"name":"l1_ratio","data_type":"float","default_value":"0.15","description":"The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1\n l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1\n Only used if `penalty` is 'elasticnet'"},{"name":"learning_rate","data_type":"str","default_value":"\"optimal\"","description":"The learning rate schedule:\n\n - 'constant': `eta = eta0`\n - 'optimal': `eta = 1.0 \/ (alpha * (t + t0))`\n where t0 is chosen by a heuristic proposed by Leon Bottou\n - 'invscaling': `eta = eta0 \/ pow(t, power_t)`\n - 'adaptive': eta = eta0, as long as the training keeps decreasing\n Each time n_iter_no_change consecutive epochs fail to decrease the\n training loss by tol or fail to increase validation score by tol if\n early_stopping is True, the current learning rate is divided by 5\n\n .. versionadded:: 0.20\n Added 'adaptive' option"},{"name":"loss","data_type":"str","default_value":"\"hinge\"","description":"The loss function to be used. Defaults to 'hinge', which gives a\n linear SVM\n\n The possible options are 'hinge', 'log', 'modified_huber',\n 'squared_hinge', 'perceptron', or a regression loss: 'squared_error',\n 'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'\n\n The 'log' loss gives logistic regression, a probabilistic classifier\n 'modified_huber' is another smooth loss that brings tolerance to\n outliers as well as probability estimates\n 'squared_hinge' is like hinge but is quadratically penalized\n 'perceptron' is the linear loss used by the perceptron algorithm\n The other losses are designed for regression but can be useful in\n classification as well; see\n :class:`~sklearn.linear_model.SGDRegressor` for a description\n\n More details about the losses formulas can be found in the\n :ref:`User Guide `\n\n .. deprecated:: 1.0\n The loss 'squared_loss' was deprecated in v1.0 and will be removed\n in version 1.2. Us..."},{"name":"max_iter","data_type":"int","default_value":"1000","description":"The maximum number of passes over the training data (aka epochs)\n It only impacts the behavior in the ``fit`` method, and not the\n :meth:`partial_fit` method\n\n .. versionadded:: 0.19"},{"name":"n_iter_no_change","data_type":"int","default_value":"5","description":"Number of iterations with no improvement to wait before stopping\n fitting\n Convergence is checked against the training loss or the\n validation loss depending on the `early_stopping` parameter\n\n .. versionadded:: 0.20\n Added 'n_iter_no_change' option"},{"name":"n_jobs","data_type":"int","default_value":"null","description":"The number of CPUs to use to do the OVA (One Versus All, for\n multi-class problems) computation\n ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context\n ``-1`` means using all processors. See :term:`Glossary `\n for more details"},{"name":"penalty","data_type":[],"default_value":"\"l2\"","description":[]},{"name":"power_t","data_type":"float","default_value":"0.5","description":"The exponent for inverse scaling learning rate [default 0.5]"},{"name":"random_state","data_type":"int","default_value":"null","description":"Used for shuffling the data, when ``shuffle`` is set to ``True``\n Pass an int for reproducible output across multiple function calls\n See :term:`Glossary `"},{"name":"shuffle","data_type":"bool","default_value":"true","description":"Whether or not the training data should be shuffled after each epoch"},{"name":"tol","data_type":"float","default_value":"0.001","description":"The stopping criterion. If it is not None, training will stop\n when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive\n epochs\n Convergence is checked against the training loss or the\n validation loss depending on the `early_stopping` parameter\n\n .. versionadded:: 0.19"},{"name":"validation_fraction","data_type":"float","default_value":"0.1","description":"The proportion of training data to set aside as validation set for\n early stopping. Must be between 0 and 1\n Only used if `early_stopping` is True\n\n .. versionadded:: 0.20\n Added 'validation_fraction' option"},{"name":"verbose","data_type":"int","default_value":"0","description":"The verbosity level"},{"name":"warm_start","data_type":"bool","default_value":"false","description":"When set to True, reuse the solution of the previous call to fit as\n initialization, otherwise, just erase the previous solution\n See :term:`the Glossary `\n\n Repeatedly calling fit or partial_fit when warm_start is True can\n result in a different solution than when calling fit a single time\n because of the way the data is shuffled\n If a dynamic learning rate is used, the learning rate is adapted\n depending on the number of samples already seen. Calling ``fit`` resets\n this counter, while ``partial_fit`` will result in increasing the\n existing counter"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.0.2"]}}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_1.0.2"]}}