10 Classification Methods From Scikit Learn We Should Know

Classifier comparison using Scikit Learn

Scikit Learn is an open source, Python based very popular machine learning library. It supports various supervised (regression and classification) and unsupervised learning models.

In this blog, we’ll use 10 well known classifiers to classify the Pima Indians Diabetes dataset (download from here and for details, refer here). Instead of going deep into the algorithms or mathematical details, we have limited our discussion on using the Scikit Learn classification methods only. We haven’t included further model optimization/hyper-parameter tuning which can be part of further detailed discussions.

The database contains the following details:

1. Number of times pregnant
2. Plasma glucose concentration a 2 hours in an oral glucose tolerance test
3. Diastolic blood pressure (mm Hg)
4. Triceps skin fold thickness (mm)
5. 2-Hour serum insulin (mu U/ml)
6. Body mass index (weight in kg/(height in m)^2)
7. Diabetes pedigree function
8. Age (years)
9. Class variable (0 or 1)

Let’s load the data and use it as a Spark table…

df = spark.table ('pima_indians_diabetes')
print(f"""There are {df.count()} records in the dataset.""")
df.show(5)