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sklearn.model_selection .train_test_split¶

Split arrays or matrices into random train and test subsets

Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)) and application to input data into a single call for splitting (and optionally subsampling) data in a oneliner.

Read more in the User Guide .

Parameters *arrays sequence of indexables with same length / shape[0]

Allowed inputs are lists, numpy arrays, scipy-sparse matrices or pandas dataframes.

test_size float or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.25.

train_size float or int, default=None

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size.

random_state int, RandomState instance or None, default=None

Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls. See Glossary .

shuffle bool, default=True

Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None.

stratify array-like, default=None

If not None, data is split in a stratified fashion, using this as the class labels. Read more in the User Guide .

Returns splitting list, length=2 * len(arrays)

List containing train-test split of inputs.

sklearn.model_selection .train_test_split¶ Split arrays or matrices into random train and test subsets Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)) and

Split-seed: a new tool for maize researchers

Affiliation

  • 1 Plant Science Research Center, The University of Toledo, 2801 West Bancroft Street, Toledo, OH 43606, USA.
  • PMID: 16489455
  • DOI: 10.1007/s00425-006-0237-9

Split-seed: a new tool for maize researchers

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Authors

Affiliation

  • 1 Plant Science Research Center, The University of Toledo, 2801 West Bancroft Street, Toledo, OH 43606, USA.
  • PMID: 16489455
  • DOI: 10.1007/s00425-006-0237-9

Abstract

Until recently, immature embryos have been a choice tissue for manipulation in culture for regeneration and production of transgenic maize plants. The utility of this explant has been compromised by low output, genotype dependence and time-consuming incubation in tissue culture. We have developed a new explant, the split-seed, which addresses these limitations by formally treating each seed as though it were a “dicot”. By splitting maize seed longitudinally, three different tissues: the scutellum, the coleoptilar-ring and the shoot apical meristems are simultaneously exposed. The cells of these tissues can be made competent to enhance the regeneration, given that the molecular networks resulting from exposure of the split-seed to hormones is likely to be different from whole seed and, in turn, affects the in vitro response. Using this explant, callus induction frequency exceeded 92% and the regeneration frequency was 76%. The mean number of shoots regenerated via callus was 11 shoots per callus clump and 28 shoots per explant at first sub-culture. All of the regenerated plants survived and were 95% fertile. The large numbers of fertile plants produced were regenerated in 6-8 weeks. Finally, the incidence of regenerated plants varies as a function of growth regulator profile.

Until recently, immature embryos have been a choice tissue for manipulation in culture for regeneration and production of transgenic maize plants. The utility of this explant has been compromised by low output, genotype dependence and time-consuming incubation in tissue culture. We have developed a …