fit_transform() takes 2 positional arguments but 3 were given with LabelBinarizer

Question

I am totally new to Machine Learning and I have been working with unsupervised learning technique.

Image shows my sample Data(After all Cleaning) Screenshot : Sample

Answer 1

I think you are going through the examples from the book: Hands on Machine Learning with Scikit Learn and Tensorflow. I ran into the same problem when going through the example in Chapter 2.

As mentioned by other people, the problem is to do with sklearn's LabelBinarizer. It takes less args in its fit_transform method compared to other transformers in the pipeline. (only y when other transformers normally take both X and y, see here for details). That's why when we run pipeline.fit_transform, we fed more args into this transformer than required.

An easy fix I used is to just use OneHotEncoder and set the "sparse" to False to ensure the output is a numpy array same as the num_pipeline output. (this way you don't need to code up your own custom encoder)

your original cat_pipeline:

cat_pipeline = Pipeline([
('selector', DataFrameSelector(cat_attribs)),
('label_binarizer', LabelBinarizer())
])

you can simply change this part to:

cat_pipeline = Pipeline([
('selector', DataFrameSelector(cat_attribs)),
('one_hot_encoder', OneHotEncoder(sparse=False))
])

You can go from here and everything should work.