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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Computer Vision & Pattern Recognition | Expert Systems |
Intelligence & Semantics | Machine Theory |
Natural Language Processing | Neural Networks |
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