Julie Michelman - Pandas, Pipelines, and Custom Transformers
Why Stack Overflow recommends it
Stack Overflow users link it when a scikit-learn Pipeline or FeatureUnion strips pandas column names and indices and they need custom transformers to keep working with DataFrames.
Recommended by 1 people across 1 different months, 2017 to 2020.
What it is
A PyData talk by Julie Michelman on combining pandas and scikit-learn. It walks through DataFrames, scikit-learn preprocessing and Pipelines, and shows how to write custom transformers so data stays in DataFrame form instead of turning into numpy arrays.
What people said
Recommends Naveen Reddy Marthala · 2020-12 · Stack Overflow · on “Using .loc inside custom transformer produces copy with slice error”
i presume problem have originated because your transformers are made to work with dataframes, while sklearn's pipelines passess numpy arrays through it's pipleines. watch this video: [this video] and read [LINK] if your problem is still not resolved, comment here.
Cites jjn · 2017-08 · Stack Overflow · on “Using CountVectorizor in FeatureUnion to use hetorogenious features”
I have a text classification problem and want to add two features. The data I have is the reviews of movies (the imbd dataset). I want to use two features as input variable to classify the reviews into either a positive or negative sentiment. The two features are: Review-length (so the words used) and Bag-of-words representation of the review. So the other thing is that I want to complete this using the FeatureUnion object in sklearn. So I figured out how to do it without, by using hstack in order to concatenate two sparse matrices. But as a learning purpose I am curious how to do it with FeatureUnion as I can not get my way around. The resources I used until now: Michelle Fullwood - Using P
Cites Christian Eslabon · 2020-06 · Stack Overflow · on “Doing a ML predict but prices appears as in CSV”
First, split your data into a train and test file. Next, train the model using the .fit() function using your x_train and y_train datasets. Then, run the .predict() function to make a prediction and assign the values as a list in the y_pred variable. Finally, Make sure not to include the column that you are trying to predict in melbourne_features. import pandas as pd from sklearn.tree import DecisionTreeRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.metrics import mean_absolute_error melbourne_file_path = 'melb_data.csv' melbourne_data = pd.read_csv(melbourne_file_path) melbourne_data = melbourne_data.dropna(axis=0) y = melbourne_data.Price #Make sure not to includ