Hands-On Ensemble Learning with Python by George Kyriakides

Author:George Kyriakides
Language: eng
Format: epub
Tags: COM016000 - COMPUTERS / Computer Vision and Pattern Recognition, COM062000 - COMPUTERS / Data Modeling and Design, COM032000 - COMPUTERS / Information Technology
Publisher: Packt Publishing
Published: 2019-07-31T07:03:12+00:00

Errors and differences for gradient boost regression

For the classification example, we use the hand-written digit classification dataset. Again, we define the n_estimators and learning_rate parameters:

# --- SECTION 1 ---

# Libraries and data loading

import numpy as np

from sklearn.datasets import load_digits

from sklearn.tree import DecisionTreeClassifier

from sklearn.ensemble import GradientBoostingClassifier

from sklearn import metrics

digits = load_digits()

train_size = 1500

train_x, train_y = digits.data[:train_size], digits.target[:train_size]

test_x, test_y = digits.data[train_size:], digits.target[train_size:]

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