Tabular Classification / Regression
Using AutoTrain, you can train a model to classify or regress tabular data easily. All you need to do is select from a list of models and upload your dataset. Parameter tuning is done automatically.
Models
The following models are available for tabular classification / regression.
- xgboost
- random_forest
- ridge
- logistic_regression
- svm
- extra_trees
- gradient_boosting
- adaboost
- decision_tree
- knn
Data Format
id,category1,category2,feature1,target
1,A,X,0.3373961604172684,1
2,B,Z,0.6481718720511972,0
3,A,Y,0.36824153984054797,1
4,B,Z,0.9571551589530464,1
5,B,Z,0.14035078041264515,1
6,C,X,0.8700872583584364,1
7,A,Y,0.4736080452737105,0
8,C,Y,0.8009107519796442,1
9,A,Y,0.5204774795512048,0
10,A,Y,0.6788795301189603,0
.
.
.Columns
Your CSV dataset must have two columns: id and target.
Parameters
class autotrain.trainers.tabular.params.TabularParams
< source >( data_path: str = None model: str = 'xgboost' username: typing.Optional[str] = None seed: int = 42 train_split: str = 'train' valid_split: typing.Optional[str] = None project_name: str = 'project-name' token: typing.Optional[str] = None push_to_hub: bool = False id_column: str = 'id' target_columns: typing.Union[typing.List[str], str] = ['target'] categorical_columns: typing.Optional[typing.List[str]] = None numerical_columns: typing.Optional[typing.List[str]] = None task: str = 'classification' num_trials: int = 10 time_limit: int = 600 categorical_imputer: typing.Optional[str] = None numerical_imputer: typing.Optional[str] = None numeric_scaler: typing.Optional[str] = None )
Parameters
- data_path (str) — Path to the dataset.
- model (str) — Name of the model to use. Default is “xgboost”.
- username (Optional[str]) — Hugging Face Username.
- seed (int) — Random seed for reproducibility. Default is 42.
- train_split (str) — Name of the training data split. Default is “train”.
- valid_split (Optional[str]) — Name of the validation data split.
- project_name (str) — Name of the output directory. Default is “project-name”.
- token (Optional[str]) — Hub Token for authentication.
- push_to_hub (bool) — Whether to push the model to the hub. Default is False.
- id_column (str) — Name of the ID column. Default is “id”.
- target_columns (Union[List[str], str]) — Target column(s) in the dataset. Default is [“target”].
- categorical_columns (Optional[List[str]]) — List of categorical columns.
- numerical_columns (Optional[List[str]]) — List of numerical columns.
- task (str) — Type of task (e.g., “classification”). Default is “classification”.
- num_trials (int) — Number of trials for hyperparameter optimization. Default is 10.
- time_limit (int) — Time limit for training in seconds. Default is 600.
- categorical_imputer (Optional[str]) — Imputer strategy for categorical columns.
- numerical_imputer (Optional[str]) — Imputer strategy for numerical columns.
- numeric_scaler (Optional[str]) — Scaler strategy for numerical columns.
TabularParams is a configuration class for tabular data training parameters.