In [92]:
import requests
from bs4 import BeautifulSoup
Выполним запрос указанной страницы и проверим, что она доступна
In [93]:
req = requests.get('https://en.wikipedia.org/wiki/Bias-variance_tradeoff')
print req
<Response [200]>
Создадим парсер для страницы и выведем содержимое страницы
In [136]:
parser = BeautifulSoup(req.text, 'lxml')
print parser.prettify()
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Category:Machine learning algorithms
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See also: the categories
<wbr>
</wbr>
<a href="/wiki/Category:Data_mining" title="Category:Data mining">
Data mining
</a>
,
<wbr>
</wbr>
<a href="/wiki/Category:Classification_algorithms" title="Category:Classification algorithms">
Classification algorithms
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, and
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</wbr>
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Decision trees
</a>
.
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Genetic algorithms
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Pages in category "Machine learning algorithms"
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The following 55 pages are in this category, out of 55 total. This list may not reflect recent changes (
<a href="/wiki/Wikipedia:FAQ/Categorization#Why_might_a_category_list_not_be_up_to_date.3F" title="Wikipedia:FAQ/Categorization">
learn more
</a>
).
</p>
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<h3>
*
</h3>
<ul>
<li>
<a href="/wiki/List_of_machine_learning_concepts" title="List of machine learning concepts">
List of machine learning concepts
</a>
</li>
</ul>
</div>
<div class="mw-category-group">
<h3>
A
</h3>
<ul>
<li>
<a href="/wiki/Almeida%E2%80%93Pineda_recurrent_backpropagation" title="Almeida–Pineda recurrent backpropagation">
Almeida–Pineda recurrent backpropagation
</a>
</li>
</ul>
</div>
<div class="mw-category-group">
<h3>
B
</h3>
<ul>
<li>
<a href="/wiki/Backpropagation" title="Backpropagation">
Backpropagation
</a>
</li>
<li>
<a href="/wiki/Bootstrap_aggregating" title="Bootstrap aggregating">
Bootstrap aggregating
</a>
</li>
</ul>
</div>
<div class="mw-category-group">
<h3>
C
</h3>
<ul>
<li>
<a href="/wiki/CN2_algorithm" title="CN2 algorithm">
CN2 algorithm
</a>
</li>
<li>
<a href="/wiki/Constructing_skill_trees" title="Constructing skill trees">
Constructing skill trees
</a>
</li>
</ul>
</div>
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<h3>
D
</h3>
<ul>
<li>
<a href="/wiki/Dehaene%E2%80%93Changeux_model" title="Dehaene–Changeux model">
Dehaene–Changeux model
</a>
</li>
<li>
<a href="/wiki/Diffusion_map" title="Diffusion map">
Diffusion map
</a>
</li>
<li>
<a href="/wiki/Dominance-based_rough_set_approach" title="Dominance-based rough set approach">
Dominance-based rough set approach
</a>
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<li>
<a href="/wiki/Dynamic_time_warping" title="Dynamic time warping">
Dynamic time warping
</a>
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<h3>
E
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<li>
<a href="/wiki/Error-driven_learning" title="Error-driven learning">
Error-driven learning
</a>
</li>
<li>
<a href="/wiki/Evolutionary_multimodal_optimization" title="Evolutionary multimodal optimization">
Evolutionary multimodal optimization
</a>
</li>
<li>
<a href="/wiki/Expectation%E2%80%93maximization_algorithm" title="Expectation–maximization algorithm">
Expectation–maximization algorithm
</a>
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F
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<a href="/wiki/FastICA" title="FastICA">
FastICA
</a>
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<a href="/wiki/Forward%E2%80%93backward_algorithm" title="Forward–backward algorithm">
Forward–backward algorithm
</a>
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<h3>
G
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<a href="/wiki/GeneRec" title="GeneRec">
GeneRec
</a>
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<a href="/wiki/Genetic_Algorithm_for_Rule_Set_Production" title="Genetic Algorithm for Rule Set Production">
Genetic Algorithm for Rule Set Production
</a>
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Growing self-organizing map
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HEXQ
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<a href="/wiki/Hyper_basis_function_network" title="Hyper basis function network">
Hyper basis function network
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<h3>
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IDistance
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<h3>
K
</h3>
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K-nearest neighbors algorithm
</a>
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Kernel methods for vector output
</a>
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Kernel principal component analysis
</a>
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<h3>
L
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<a href="/wiki/Leabra" title="Leabra">
Leabra
</a>
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<a href="/wiki/Linde%E2%80%93Buzo%E2%80%93Gray_algorithm" title="Linde–Buzo–Gray algorithm">
Linde–Buzo–Gray algorithm
</a>
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<li>
<a href="/wiki/Local_outlier_factor" title="Local outlier factor">
Local outlier factor
</a>
</li>
<li>
<a href="/wiki/Logic_learning_machine" title="Logic learning machine">
Logic learning machine
</a>
</li>
<li>
<a href="/wiki/LogitBoost" title="LogitBoost">
LogitBoost
</a>
</li>
<li>
<a href="/wiki/Loss_functions_for_classification" title="Loss functions for classification">
Loss functions for classification
</a>
</li>
</ul>
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<div class="mw-category-group">
<h3>
M
</h3>
<ul>
<li>
<a href="/wiki/Manifold_alignment" title="Manifold alignment">
Manifold alignment
</a>
</li>
<li>
<a href="/wiki/Minimum_redundancy_feature_selection" title="Minimum redundancy feature selection">
Minimum redundancy feature selection
</a>
</li>
<li>
<a href="/wiki/Multiple_kernel_learning" title="Multiple kernel learning">
Multiple kernel learning
</a>
</li>
</ul>
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<h3>
N
</h3>
<ul>
<li>
<a href="/wiki/Non-negative_matrix_factorization" title="Non-negative matrix factorization">
Non-negative matrix factorization
</a>
</li>
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O
</h3>
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<li>
<a href="/wiki/Online_machine_learning" title="Online machine learning">
Online machine learning
</a>
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<a href="/wiki/Out-of-bag_error" title="Out-of-bag error">
Out-of-bag error
</a>
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<h3>
P
</h3>
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<li>
<a href="/wiki/Prefrontal_cortex_basal_ganglia_working_memory" title="Prefrontal cortex basal ganglia working memory">
Prefrontal cortex basal ganglia working memory
</a>
</li>
<li>
<a href="/wiki/PVLV" title="PVLV">
PVLV
</a>
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<h3>
Q
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<li>
<a href="/wiki/Q-learning" title="Q-learning">
Q-learning
</a>
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<li>
<a href="/wiki/Quadratic_unconstrained_binary_optimization" title="Quadratic unconstrained binary optimization">
Quadratic unconstrained binary optimization
</a>
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Query level feature
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<li>
<a href="/wiki/Radial_basis_function_network" title="Radial basis function network">
Radial basis function network
</a>
</li>
<li>
<a href="/wiki/Randomized_weighted_majority_algorithm" title="Randomized weighted majority algorithm">
Randomized weighted majority algorithm
</a>
</li>
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<a href="/wiki/Reinforcement_learning" title="Reinforcement learning">
Reinforcement learning
</a>
</li>
<li>
<a href="/wiki/Rprop" title="Rprop">
Rprop
</a>
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<a href="/wiki/Rule-based_machine_learning" title="Rule-based machine learning">
Rule-based machine learning
</a>
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<h3>
S
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<li>
<a href="/wiki/Skill_chaining" title="Skill chaining">
Skill chaining
</a>
</li>
<li>
<a href="/wiki/Sparse_PCA" title="Sparse PCA">
Sparse PCA
</a>
</li>
<li>
<a href="/wiki/State-Action-Reward-State-Action" title="State-Action-Reward-State-Action">
State-Action-Reward-State-Action
</a>
</li>
<li>
<a href="/wiki/Stochastic_gradient_descent" title="Stochastic gradient descent">
Stochastic gradient descent
</a>
</li>
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<h3>
T
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<li>
<a href="/wiki/T-distributed_stochastic_neighbor_embedding" title="T-distributed stochastic neighbor embedding">
T-distributed stochastic neighbor embedding
</a>
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<a href="/wiki/Temporal_difference_learning" title="Temporal difference learning">
Temporal difference learning
</a>
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W
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<a href="/wiki/Wake-sleep_algorithm" title="Wake-sleep algorithm">
Wake-sleep algorithm
</a>
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<li>
<a href="/wiki/Weighted_Majority_Algorithm" title="Weighted Majority Algorithm">
Weighted Majority Algorithm
</a>
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Зададим функцию для печати значений нужного тега:
In [95]:
def print_tags(parser, key='h1'):
for tag in parser.find_all(key):
print tag.text
Так как из текста задания не совсем понятно, какие заголовки считать заголовками верхнего уровня, выведем отдельно все заголовки для первого, второго и третьего уровней.
In [96]:
print_tags(parser, 'h1')
Bias–variance tradeoff
In [130]:
for t in parser.find_all('h2'):
if t.span is not None and 'class' in t.span.attrs and 'mw-headline' in t.span.attrs['class']:
print t.span.text
Motivation
Bias–variance decomposition of squared error
Application to regression
Application to classification
Approaches
Application to human learning
See also
References
External links
In [131]:
for t in parser.find_all('h3'):
if t.span is not None and 'class' in t.span.attrs and 'mw-headline' in t.span.attrs['class']:
print t.span.text
Derivation
K-nearest neighbors
Выполним запрос указанной страницы и проверим, что она доступна
In [132]:
req = requests.get('https://en.wikipedia.org/wiki/Category:Machine_learning_algorithms')
print req
<Response [200]>
Создадим парсер для страницы и выведем содержимое страницы
In [133]:
parser = BeautifulSoup(req.text, 'lxml')
print parser.prettify()
<!DOCTYPE html>
<html class="client-nojs" dir="ltr" lang="en">
<head>
<meta charset="utf-8"/>
<title>
Category:Machine learning algorithms - Wikipedia
</title>
<script>
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Category:Machine learning algorithms
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See also: the categories
<wbr>
</wbr>
<a href="/wiki/Category:Data_mining" title="Category:Data mining">
Data mining
</a>
,
<wbr>
</wbr>
<a href="/wiki/Category:Classification_algorithms" title="Category:Classification algorithms">
Classification algorithms
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, and
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</wbr>
<a href="/wiki/Category:Decision_trees" title="Category:Decision trees">
Decision trees
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Genetic algorithms
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(2 C, 41 P)
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Pages in category "Machine learning algorithms"
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The following 55 pages are in this category, out of 55 total. This list may not reflect recent changes (
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learn more
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<h3>
*
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<ul>
<li>
<a href="/wiki/List_of_machine_learning_concepts" title="List of machine learning concepts">
List of machine learning concepts
</a>
</li>
</ul>
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<h3>
A
</h3>
<ul>
<li>
<a href="/wiki/Almeida%E2%80%93Pineda_recurrent_backpropagation" title="Almeida–Pineda recurrent backpropagation">
Almeida–Pineda recurrent backpropagation
</a>
</li>
</ul>
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<h3>
B
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<li>
<a href="/wiki/Backpropagation" title="Backpropagation">
Backpropagation
</a>
</li>
<li>
<a href="/wiki/Bootstrap_aggregating" title="Bootstrap aggregating">
Bootstrap aggregating
</a>
</li>
</ul>
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<h3>
C
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<li>
<a href="/wiki/CN2_algorithm" title="CN2 algorithm">
CN2 algorithm
</a>
</li>
<li>
<a href="/wiki/Constructing_skill_trees" title="Constructing skill trees">
Constructing skill trees
</a>
</li>
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<h3>
D
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<li>
<a href="/wiki/Dehaene%E2%80%93Changeux_model" title="Dehaene–Changeux model">
Dehaene–Changeux model
</a>
</li>
<li>
<a href="/wiki/Diffusion_map" title="Diffusion map">
Diffusion map
</a>
</li>
<li>
<a href="/wiki/Dominance-based_rough_set_approach" title="Dominance-based rough set approach">
Dominance-based rough set approach
</a>
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<li>
<a href="/wiki/Dynamic_time_warping" title="Dynamic time warping">
Dynamic time warping
</a>
</li>
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<h3>
E
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<li>
<a href="/wiki/Error-driven_learning" title="Error-driven learning">
Error-driven learning
</a>
</li>
<li>
<a href="/wiki/Evolutionary_multimodal_optimization" title="Evolutionary multimodal optimization">
Evolutionary multimodal optimization
</a>
</li>
<li>
<a href="/wiki/Expectation%E2%80%93maximization_algorithm" title="Expectation–maximization algorithm">
Expectation–maximization algorithm
</a>
</li>
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<h3>
F
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<li>
<a href="/wiki/FastICA" title="FastICA">
FastICA
</a>
</li>
<li>
<a href="/wiki/Forward%E2%80%93backward_algorithm" title="Forward–backward algorithm">
Forward–backward algorithm
</a>
</li>
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<h3>
G
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<li>
<a href="/wiki/GeneRec" title="GeneRec">
GeneRec
</a>
</li>
<li>
<a href="/wiki/Genetic_Algorithm_for_Rule_Set_Production" title="Genetic Algorithm for Rule Set Production">
Genetic Algorithm for Rule Set Production
</a>
</li>
<li>
<a href="/wiki/Growing_self-organizing_map" title="Growing self-organizing map">
Growing self-organizing map
</a>
</li>
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<h3>
H
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<a href="/wiki/HEXQ" title="HEXQ">
HEXQ
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<a href="/wiki/Hyper_basis_function_network" title="Hyper basis function network">
Hyper basis function network
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<h3>
I
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<a href="/wiki/IDistance" title="IDistance">
IDistance
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<h3>
K
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<li>
<a href="/wiki/K-nearest_neighbors_algorithm" title="K-nearest neighbors algorithm">
K-nearest neighbors algorithm
</a>
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<li>
<a href="/wiki/Kernel_methods_for_vector_output" title="Kernel methods for vector output">
Kernel methods for vector output
</a>
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<a href="/wiki/Kernel_principal_component_analysis" title="Kernel principal component analysis">
Kernel principal component analysis
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<h3>
L
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<li>
<a href="/wiki/Leabra" title="Leabra">
Leabra
</a>
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<li>
<a href="/wiki/Linde%E2%80%93Buzo%E2%80%93Gray_algorithm" title="Linde–Buzo–Gray algorithm">
Linde–Buzo–Gray algorithm
</a>
</li>
<li>
<a href="/wiki/Local_outlier_factor" title="Local outlier factor">
Local outlier factor
</a>
</li>
<li>
<a href="/wiki/Logic_learning_machine" title="Logic learning machine">
Logic learning machine
</a>
</li>
<li>
<a href="/wiki/LogitBoost" title="LogitBoost">
LogitBoost
</a>
</li>
<li>
<a href="/wiki/Loss_functions_for_classification" title="Loss functions for classification">
Loss functions for classification
</a>
</li>
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<h3>
M
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<li>
<a href="/wiki/Manifold_alignment" title="Manifold alignment">
Manifold alignment
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<li>
<a href="/wiki/Minimum_redundancy_feature_selection" title="Minimum redundancy feature selection">
Minimum redundancy feature selection
</a>
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<li>
<a href="/wiki/Multiple_kernel_learning" title="Multiple kernel learning">
Multiple kernel learning
</a>
</li>
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<h3>
N
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<li>
<a href="/wiki/Non-negative_matrix_factorization" title="Non-negative matrix factorization">
Non-negative matrix factorization
</a>
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<h3>
O
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<li>
<a href="/wiki/Online_machine_learning" title="Online machine learning">
Online machine learning
</a>
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<a href="/wiki/Out-of-bag_error" title="Out-of-bag error">
Out-of-bag error
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<h3>
P
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<li>
<a href="/wiki/Prefrontal_cortex_basal_ganglia_working_memory" title="Prefrontal cortex basal ganglia working memory">
Prefrontal cortex basal ganglia working memory
</a>
</li>
<li>
<a href="/wiki/PVLV" title="PVLV">
PVLV
</a>
</li>
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Q
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<a href="/wiki/Q-learning" title="Q-learning">
Q-learning
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<a href="/wiki/Quadratic_unconstrained_binary_optimization" title="Quadratic unconstrained binary optimization">
Quadratic unconstrained binary optimization
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Query level feature
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Quickprop
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<a href="/wiki/Radial_basis_function_network" title="Radial basis function network">
Radial basis function network
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<a href="/wiki/Randomized_weighted_majority_algorithm" title="Randomized weighted majority algorithm">
Randomized weighted majority algorithm
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Reinforcement learning
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Rprop
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Rule-based machine learning
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S
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<a href="/wiki/Skill_chaining" title="Skill chaining">
Skill chaining
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<a href="/wiki/Sparse_PCA" title="Sparse PCA">
Sparse PCA
</a>
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<a href="/wiki/State-Action-Reward-State-Action" title="State-Action-Reward-State-Action">
State-Action-Reward-State-Action
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Stochastic gradient descent
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T
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<a href="/wiki/T-distributed_stochastic_neighbor_embedding" title="T-distributed stochastic neighbor embedding">
T-distributed stochastic neighbor embedding
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Temporal difference learning
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W
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<a href="/wiki/Wake-sleep_algorithm" title="Wake-sleep algorithm">
Wake-sleep algorithm
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<a href="/wiki/Weighted_Majority_Algorithm" title="Weighted Majority Algorithm">
Weighted Majority Algorithm
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Изучив структуру верстки страницы, определим тег, содержащий интересующие нас заголовки
In [134]:
pages = parser.find(string='Pages in category "Machine learning algorithms"')
data_parent = pages.parent.parent.div.div
In [135]:
print_tags(data_parent, 'a')
List of machine learning concepts
Almeida–Pineda recurrent backpropagation
Backpropagation
Bootstrap aggregating
CN2 algorithm
Constructing skill trees
Dehaene–Changeux model
Diffusion map
Dominance-based rough set approach
Dynamic time warping
Error-driven learning
Evolutionary multimodal optimization
Expectation–maximization algorithm
FastICA
Forward–backward algorithm
GeneRec
Genetic Algorithm for Rule Set Production
Growing self-organizing map
HEXQ
Hyper basis function network
IDistance
K-nearest neighbors algorithm
Kernel methods for vector output
Kernel principal component analysis
Leabra
Linde–Buzo–Gray algorithm
Local outlier factor
Logic learning machine
LogitBoost
Loss functions for classification
Manifold alignment
Minimum redundancy feature selection
Multiple kernel learning
Non-negative matrix factorization
Online machine learning
Out-of-bag error
Prefrontal cortex basal ganglia working memory
PVLV
Q-learning
Quadratic unconstrained binary optimization
Query level feature
Quickprop
Radial basis function network
Randomized weighted majority algorithm
Reinforcement learning
Rprop
Rule-based machine learning
Skill chaining
Sparse PCA
State-Action-Reward-State-Action
Stochastic gradient descent
T-distributed stochastic neighbor embedding
Temporal difference learning
Wake-sleep algorithm
Weighted Majority Algorithm
In [ ]:
Content source: astarostin/MachineLearningSpecializationCoursera
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