What is ParallelPostFit in dask?
This recipe explains 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)
What Users are saying..
Gautam Vermani
Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic... Read More