What is ParallelPostFit in dask?

Recipe Objective.

What is ParallelPostFit in dask?

Dask has meta-estimators which parallelize and scale-out many computations that can't be performed in the sci-kit.

``` wrappers.ParallelPostFit ```

wrappers.ParallelPostFit is the meta-estimator to parallelize post-fit tasks like transformation and prediction.

ParallelPostFit does not parallelize the training step.

wrappers.ParallelPostFit is useful in many situations where our training dataset is relatively smaller than usual dataset, and prediction or transformation must be done on a much larger dataset.

Build a Multi Touch Attribution Model in Python with Source Code

Step 1- Importing Libraries.

#!pip install dask_ml from sklearn.ensemble import GradientBoostingClassifier import sklearn.datasets import dask_ml.datasets from dask_ml.wrappers import ParallelPostFit

Step 2- Classifying the dataset.

X, y = sklearn.datasets.make_classification(n_samples=100, random_state=0)

Step 3- Applying parallelPostFit.

clf = ParallelPostFit(estimator=GradientBoostingClassifier()) clf.fit(X, y)