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XGBoost Templates

  • Using sklearn and XGboost for classification
  • Applying gblinear

Dataset: UCI Machine Learning Repository Iris dataset
Dataset: Diabetes Diabetes dataset
Dataset: Heart desease

!pip install jovian --upgrade --quiet
import jovian
jovian.commit(project='xgboost-iris-diabetes', filename='xgboost-iris-diabetes.ipynb')
[jovian] Attempting to save notebook.. [jovian] Updating notebook "patxigad/xgboost-iris-diabetes" on https://jovian.ai/ [jovian] Uploading notebook.. [jovian] Capturing environment.. [jovian] Committed successfully! https://jovian.ai/patxigad/xgboost-iris-diabetes

Getting the data

# main imports
import pandas as pd
import datetime as dt
import numpy as np

import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

matplotlib.rcParams['font.size'] = 14
matplotlib.rcParams['figure.figsize'] = (9, 5)
matplotlib.rcParams['figure.facecolor'] = '#00000000'

# silence warnings
import warnings
# import the data from sklearn
from sklearn import datasets

# load the iris dataset
iris = datasets.load_iris()

# create the dataframe
df = pd.DataFrame(data=np.c_[iris['data'],
                  columns=iris['feature_names'] + ['target'])

# explore first five rows