Learn how to use scikit-learn pipelines to compose steps in the learning process. You can take data, scale it, reduce dimensionality and then run support vector machine on it. You can also learn how to set parameters for individual components of the pipel
- [Instructor] As we saw, there might be several steps…we need to perform before we finally see the algorithm.…Sometimes, we might have post-processing steps, as well.…Since this is very common, Scikit Learn…provides us pipelines, which are…a way to group together several steps.…Let's create one.…We first want to scale the data,…then reduce the number of dimensions,…and then use SVR.…Before, we used the scale function.…However, a pipeline accepts an object with a non-API.…For this, we have standard scalar…in the pre-processing model.…
So, let's import.…From sklearn.preprocessing, we import StandardScalar.…And, from sklearn.pipeline, we import Pipeline.…Now, let's create the pipeline.…So, pipe equals Pipeline.…And we'll provide steps.…For each step, we say the name.…For example, scale and then StandardScalar,…and then we'll have a step of pca,…and you will use pca with n_components equal five.…
And then, we'll use our own SVR.…So, it's just a standard SVR.…This pipe object behaves like most other models…in Scikit Learn.…
- Working with Jupyter notebooks
- Using code cells
- Extensions to the Python language
- Markdown cells
- Editing notebooks
- NumPy basics
- Broadcasting, array operations, and ufuncs
- Folium and Geo
- Machine learning with scikit-learn
- Plotting with matplotlib and bokeh
- Branching into Numba, Cython, deep learning, and NLP
Skill Level Intermediate
NumPy Data Science Essential Trainingwith Charles Kelly3h 54m Intermediate
1. Scientific Python Overview
2. The Jupyter Notebook
3. NumPy Basics
Manage environments5m 11s
6. Folium and Geo
7. NY Taxi Data
10. Other Packages
11. Development Process
Next steps1m 33s
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