2021-11-09 15:50:53 +01:00
<!doctype html>
2023-02-08 19:06:53 +01:00
< html lang = "en" >
2021-11-09 15:50:53 +01:00
< head >
< meta charset = "utf-8" / >
2023-02-08 19:06:53 +01:00
< meta name = "viewport" content = "width=device-width, initial-scale=1.0" / > < meta name = "generator" content = "Docutils 0.19: https://docutils.sourceforge.io/" / >
< title > Model Selection — QuaPy 0.1.7 documentation< / title >
2021-11-09 15:50:53 +01:00
< link rel = "stylesheet" type = "text/css" href = "_static/pygments.css" / >
< link rel = "stylesheet" type = "text/css" href = "_static/bizstyle.css" / >
< script data-url_root = "./" id = "documentation_options" src = "_static/documentation_options.js" > < / script >
< script src = "_static/jquery.js" > < / script >
< script src = "_static/underscore.js" > < / script >
2023-02-08 19:06:53 +01:00
< script src = "_static/_sphinx_javascript_frameworks_compat.js" > < / script >
2021-11-09 15:50:53 +01:00
< script src = "_static/doctools.js" > < / script >
2023-02-08 19:06:53 +01:00
< script src = "_static/sphinx_highlight.js" > < / script >
2021-11-09 15:50:53 +01:00
< script src = "_static/bizstyle.js" > < / script >
< link rel = "index" title = "Index" href = "genindex.html" / >
< link rel = "search" title = "Search" href = "search.html" / >
2023-02-08 19:06:53 +01:00
< link rel = "next" title = "Plotting" href = "Plotting.html" / >
< link rel = "prev" title = "Quantification Methods" href = "Methods.html" / >
2021-11-09 15:50:53 +01:00
< meta name = "viewport" content = "width=device-width,initial-scale=1.0" / >
<!-- [if lt IE 9]>
< script src = "_static/css3-mediaqueries.js" > < / script >
<![endif]-->
< / head > < body >
< div class = "related" role = "navigation" aria-label = "related navigation" >
< h3 > Navigation< / h3 >
< ul >
< li class = "right" style = "margin-right: 10px" >
< a href = "genindex.html" title = "General Index"
accesskey="I">index< / a > < / li >
< li class = "right" >
< a href = "py-modindex.html" title = "Python Module Index"
>modules< / a > |< / li >
2023-02-08 19:06:53 +01:00
< li class = "right" >
< a href = "Plotting.html" title = "Plotting"
accesskey="N">next< / a > |< / li >
< li class = "right" >
< a href = "Methods.html" title = "Quantification Methods"
accesskey="P">previous< / a > |< / li >
< li class = "nav-item nav-item-0" > < a href = "index.html" > QuaPy 0.1.7 documentation< / a > » < / li >
2021-11-09 15:50:53 +01:00
< li class = "nav-item nav-item-this" > < a href = "" > Model Selection< / a > < / li >
< / ul >
< / div >
< div class = "document" >
< div class = "documentwrapper" >
< div class = "bodywrapper" >
< div class = "body" role = "main" >
2023-02-08 19:06:53 +01:00
< section id = "model-selection" >
< h1 > Model Selection< a class = "headerlink" href = "#model-selection" title = "Permalink to this heading" > ¶< / a > < / h1 >
2021-11-09 15:50:53 +01:00
< p > As a supervised machine learning task, quantification methods
can strongly depend on a good choice of model hyper-parameters.
The process whereby those hyper-parameters are chosen is
typically known as < em > Model Selection< / em > , and typically consists of
testing different settings and picking the one that performed
best in a held-out validation set in terms of any given
evaluation measure.< / p >
2023-02-08 19:06:53 +01:00
< section id = "targeting-a-quantification-oriented-loss" >
< h2 > Targeting a Quantification-oriented loss< a class = "headerlink" href = "#targeting-a-quantification-oriented-loss" title = "Permalink to this heading" > ¶< / a > < / h2 >
2021-11-09 15:50:53 +01:00
< p > The task being optimized determines the evaluation protocol,
i.e., the criteria according to which the performance of
any given method for solving is to be assessed.
As a task on its own right, quantification should impose
its own model selection strategies, i.e., strategies
aimed at finding appropriate configurations
specifically designed for the task of quantification.< / p >
< p > Quantification has long been regarded as an add-on of
classification, and thus the model selection strategies
customarily adopted in classification have simply been
applied to quantification (see the next section).
2023-02-14 17:00:50 +01:00
It has been argued in < a class = "reference external" href = "https://link.springer.com/chapter/10.1007/978-3-030-72240-1_6" > Moreo, Alejandro, and Fabrizio Sebastiani.
Re-Assessing the “Classify and Count” Quantification Method.
ECIR 2021: Advances in Information Retrieval pp 75– < / a >
2021-11-09 15:50:53 +01:00
that specific model selection strategies should
be adopted for quantification. That is, model selection
strategies for quantification should target
quantification-oriented losses and be tested in a variety
of scenarios exhibiting different degrees of prior
probability shift.< / p >
2023-02-14 17:00:50 +01:00
< p > The class < em > qp.model_selection.GridSearchQ< / em > implements a grid-search exploration over the space of
hyper-parameter combinations that < a class = "reference external" href = "https://github.com/HLT-ISTI/QuaPy/wiki/Evaluation" > evaluates< / a >
each combination of hyper-parameters by means of a given quantification-oriented
2021-11-09 15:50:53 +01:00
error metric (e.g., any of the error functions implemented
2023-02-14 17:00:50 +01:00
in < em > qp.error< / em > ) and according to a
< a class = "reference external" href = "https://github.com/HLT-ISTI/QuaPy/wiki/Protocols" > sampling generation protocol< / a > .< / p >
< p > The following is an example (also included in the examples folder) of model selection for quantification:< / p >
2021-11-09 15:50:53 +01:00
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "kn" > import< / span > < span class = "nn" > quapy< / span > < span class = "k" > as< / span > < span class = "nn" > qp< / span >
2023-02-14 17:00:50 +01:00
< span class = "kn" > from< / span > < span class = "nn" > quapy.protocol< / span > < span class = "kn" > import< / span > < span class = "n" > APP< / span >
< span class = "kn" > from< / span > < span class = "nn" > quapy.method.aggregative< / span > < span class = "kn" > import< / span > < span class = "n" > DistributionMatching< / span >
2021-11-09 15:50:53 +01:00
< span class = "kn" > from< / span > < span class = "nn" > sklearn.linear_model< / span > < span class = "kn" > import< / span > < span class = "n" > LogisticRegression< / span >
< span class = "kn" > import< / span > < span class = "nn" > numpy< / span > < span class = "k" > as< / span > < span class = "nn" > np< / span >
2023-02-14 17:00:50 +01:00
< span class = "sd" > " " " < / span >
< span class = "sd" > In this example, we show how to perform model selection on a DistributionMatching quantifier.< / span >
< span class = "sd" > " " " < / span >
2021-11-09 15:50:53 +01:00
2023-02-14 17:00:50 +01:00
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > DistributionMatching< / span > < span class = "p" > (< / span > < span class = "n" > LogisticRegression< / span > < span class = "p" > ())< / span >
< span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > environ< / span > < span class = "p" > [< / span > < span class = "s1" > ' SAMPLE_SIZE' < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "mi" > 100< / span >
< span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > environ< / span > < span class = "p" > [< / span > < span class = "s1" > ' N_JOBS' < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "o" > -< / span > < span class = "mi" > 1< / span > < span class = "c1" > # explore hyper-parameters in parallel< / span >
< span class = "n" > training< / span > < span class = "p" > ,< / span > < span class = "n" > test< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > datasets< / span > < span class = "o" > .< / span > < span class = "n" > fetch_reviews< / span > < span class = "p" > (< / span > < span class = "s1" > ' imdb' < / span > < span class = "p" > ,< / span > < span class = "n" > tfidf< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "n" > min_df< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > train_test< / span >
2021-11-09 15:50:53 +01:00
< span class = "c1" > # The model will be returned by the fit method of GridSearchQ.< / span >
2023-02-14 17:00:50 +01:00
< span class = "c1" > # Every combination of hyper-parameters will be evaluated by confronting the< / span >
< span class = "c1" > # quantifier thus configured against a series of samples generated by means< / span >
< span class = "c1" > # of a sample generation protocol. For this example, we will use the< / span >
< span class = "c1" > # artificial-prevalence protocol (APP), that generates samples with prevalence< / span >
< span class = "c1" > # values in the entire range of values from a grid (e.g., [0, 0.1, 0.2, ..., 1]).< / span >
< span class = "c1" > # We devote 30% of the dataset for this exploration.< / span >
< span class = "n" > training< / span > < span class = "p" > ,< / span > < span class = "n" > validation< / span > < span class = "o" > =< / span > < span class = "n" > training< / span > < span class = "o" > .< / span > < span class = "n" > split_stratified< / span > < span class = "p" > (< / span > < span class = "n" > train_prop< / span > < span class = "o" > =< / span > < span class = "mf" > 0.7< / span > < span class = "p" > )< / span >
< span class = "n" > protocol< / span > < span class = "o" > =< / span > < span class = "n" > APP< / span > < span class = "p" > (< / span > < span class = "n" > validation< / span > < span class = "p" > )< / span >
< span class = "c1" > # We will explore a classification-dependent hyper-parameter (e.g., the ' C' < / span >
< span class = "c1" > # hyper-parameter of LogisticRegression) and a quantification-dependent hyper-parameter< / span >
< span class = "c1" > # (e.g., the number of bins in a DistributionMatching quantifier.< / span >
< span class = "c1" > # Classifier-dependent hyper-parameters have to be marked with a prefix " classifier__" < / span >
< span class = "c1" > # in order to let the quantifier know this hyper-parameter belongs to its underlying< / span >
< span class = "c1" > # classifier.< / span >
< span class = "n" > param_grid< / span > < span class = "o" > =< / span > < span class = "p" > {< / span >
< span class = "s1" > ' classifier__C' < / span > < span class = "p" > :< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > logspace< / span > < span class = "p" > (< / span > < span class = "o" > -< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 3< / span > < span class = "p" > ,< / span > < span class = "mi" > 7< / span > < span class = "p" > ),< / span >
< span class = "s1" > ' nbins' < / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "mi" > 8< / span > < span class = "p" > ,< / span > < span class = "mi" > 16< / span > < span class = "p" > ,< / span > < span class = "mi" > 32< / span > < span class = "p" > ,< / span > < span class = "mi" > 64< / span > < span class = "p" > ],< / span >
< span class = "p" > }< / span >
2021-11-09 15:50:53 +01:00
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > model_selection< / span > < span class = "o" > .< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > (< / span >
2023-02-14 17:00:50 +01:00
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span >
< span class = "n" > param_grid< / span > < span class = "o" > =< / span > < span class = "n" > param_grid< / span > < span class = "p" > ,< / span >
< span class = "n" > protocol< / span > < span class = "o" > =< / span > < span class = "n" > protocol< / span > < span class = "p" > ,< / span >
< span class = "n" > error< / span > < span class = "o" > =< / span > < span class = "s1" > ' mae' < / span > < span class = "p" > ,< / span > < span class = "c1" > # the error to optimize is the MAE (a quantification-oriented loss)< / span >
< span class = "n" > refit< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "p" > ,< / span > < span class = "c1" > # retrain on the whole labelled set once done< / span >
2021-11-09 15:50:53 +01:00
< span class = "n" > verbose< / span > < span class = "o" > =< / span > < span class = "kc" > True< / span > < span class = "c1" > # show information as the process goes on< / span >
2023-02-14 17:00:50 +01:00
< span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > training< / span > < span class = "p" > )< / span >
2021-11-09 15:50:53 +01:00
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "sa" > f< / span > < span class = "s1" > ' model selection ended: best hyper-parameters=< / span > < span class = "si" > {< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > best_params_< / span > < span class = "si" > }< / span > < span class = "s1" > ' < / span > < span class = "p" > )< / span >
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > model< / span > < span class = "o" > .< / span > < span class = "n" > best_model_< / span >
< span class = "c1" > # evaluation in terms of MAE< / span >
2023-02-14 17:00:50 +01:00
< span class = "c1" > # we use the same evaluation protocol (APP) on the test set< / span >
< span class = "n" > mae_score< / span > < span class = "o" > =< / span > < span class = "n" > qp< / span > < span class = "o" > .< / span > < span class = "n" > evaluation< / span > < span class = "o" > .< / span > < span class = "n" > evaluate< / span > < span class = "p" > (< / span > < span class = "n" > model< / span > < span class = "p" > ,< / span > < span class = "n" > protocol< / span > < span class = "o" > =< / span > < span class = "n" > APP< / span > < span class = "p" > (< / span > < span class = "n" > test< / span > < span class = "p" > ),< / span > < span class = "n" > error_metric< / span > < span class = "o" > =< / span > < span class = "s1" > ' mae' < / span > < span class = "p" > )< / span >
< span class = "nb" > print< / span > < span class = "p" > (< / span > < span class = "sa" > f< / span > < span class = "s1" > ' MAE=< / span > < span class = "si" > {< / span > < span class = "n" > mae_score< / span > < span class = "si" > :< / span > < span class = "s1" > .5f< / span > < span class = "si" > }< / span > < span class = "s1" > ' < / span > < span class = "p" > )< / span >
2021-11-09 15:50:53 +01:00
< / pre > < / div >
< / div >
< p > In this example, the system outputs:< / p >
2023-02-14 17:00:50 +01:00
< div class = "highlight-default notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "p" > [< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > ]:< / span > < span class = "n" > starting< / span > < span class = "n" > model< / span > < span class = "n" > selection< / span > < span class = "k" > with< / span > < span class = "bp" > self< / span > < span class = "o" > .< / span > < span class = "n" > n_jobs< / span > < span class = "o" > =-< / span > < span class = "mi" > 1< / span >
< span class = "p" > [< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > ]:< / span > < span class = "n" > hyperparams< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s1" > ' classifier__C' < / span > < span class = "p" > :< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "s1" > ' nbins' < / span > < span class = "p" > :< / span > < span class = "mi" > 64< / span > < span class = "p" > }< / span > < span class = "n" > got< / span > < span class = "n" > mae< / span > < span class = "n" > score< / span > < span class = "mf" > 0.04021< / span > < span class = "p" > [< / span > < span class = "n" > took< / span > < span class = "mf" > 1.1356< / span > < span class = "n" > s< / span > < span class = "p" > ]< / span >
< span class = "p" > [< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > ]:< / span > < span class = "n" > hyperparams< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s1" > ' classifier__C' < / span > < span class = "p" > :< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "s1" > ' nbins' < / span > < span class = "p" > :< / span > < span class = "mi" > 32< / span > < span class = "p" > }< / span > < span class = "n" > got< / span > < span class = "n" > mae< / span > < span class = "n" > score< / span > < span class = "mf" > 0.04286< / span > < span class = "p" > [< / span > < span class = "n" > took< / span > < span class = "mf" > 1.2139< / span > < span class = "n" > s< / span > < span class = "p" > ]< / span >
< span class = "p" > [< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > ]:< / span > < span class = "n" > hyperparams< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s1" > ' classifier__C' < / span > < span class = "p" > :< / span > < span class = "mf" > 0.01< / span > < span class = "p" > ,< / span > < span class = "s1" > ' nbins' < / span > < span class = "p" > :< / span > < span class = "mi" > 16< / span > < span class = "p" > }< / span > < span class = "n" > got< / span > < span class = "n" > mae< / span > < span class = "n" > score< / span > < span class = "mf" > 0.04888< / span > < span class = "p" > [< / span > < span class = "n" > took< / span > < span class = "mf" > 1.2491< / span > < span class = "n" > s< / span > < span class = "p" > ]< / span >
< span class = "p" > [< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > ]:< / span > < span class = "n" > hyperparams< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s1" > ' classifier__C' < / span > < span class = "p" > :< / span > < span class = "mf" > 0.001< / span > < span class = "p" > ,< / span > < span class = "s1" > ' nbins' < / span > < span class = "p" > :< / span > < span class = "mi" > 8< / span > < span class = "p" > }< / span > < span class = "n" > got< / span > < span class = "n" > mae< / span > < span class = "n" > score< / span > < span class = "mf" > 0.05163< / span > < span class = "p" > [< / span > < span class = "n" > took< / span > < span class = "mf" > 1.5372< / span > < span class = "n" > s< / span > < span class = "p" > ]< / span >
< span class = "p" > [< / span > < span class = "o" > ...< / span > < span class = "p" > ]< / span >
< span class = "p" > [< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > ]:< / span > < span class = "n" > hyperparams< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s1" > ' classifier__C' < / span > < span class = "p" > :< / span > < span class = "mf" > 1000.0< / span > < span class = "p" > ,< / span > < span class = "s1" > ' nbins' < / span > < span class = "p" > :< / span > < span class = "mi" > 32< / span > < span class = "p" > }< / span > < span class = "n" > got< / span > < span class = "n" > mae< / span > < span class = "n" > score< / span > < span class = "mf" > 0.02445< / span > < span class = "p" > [< / span > < span class = "n" > took< / span > < span class = "mf" > 2.9056< / span > < span class = "n" > s< / span > < span class = "p" > ]< / span >
< span class = "p" > [< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > ]:< / span > < span class = "n" > optimization< / span > < span class = "n" > finished< / span > < span class = "p" > :< / span > < span class = "n" > best< / span > < span class = "n" > params< / span > < span class = "p" > {< / span > < span class = "s1" > ' classifier__C' < / span > < span class = "p" > :< / span > < span class = "mf" > 100.0< / span > < span class = "p" > ,< / span > < span class = "s1" > ' nbins' < / span > < span class = "p" > :< / span > < span class = "mi" > 32< / span > < span class = "p" > }< / span > < span class = "p" > (< / span > < span class = "n" > score< / span > < span class = "o" > =< / span > < span class = "mf" > 0.02234< / span > < span class = "p" > )< / span > < span class = "p" > [< / span > < span class = "n" > took< / span > < span class = "mf" > 7.3114< / span > < span class = "n" > s< / span > < span class = "p" > ]< / span >
< span class = "p" > [< / span > < span class = "n" > GridSearchQ< / span > < span class = "p" > ]:< / span > < span class = "n" > refitting< / span > < span class = "n" > on< / span > < span class = "n" > the< / span > < span class = "n" > whole< / span > < span class = "n" > development< / span > < span class = "nb" > set< / span >
< span class = "n" > model< / span > < span class = "n" > selection< / span > < span class = "n" > ended< / span > < span class = "p" > :< / span > < span class = "n" > best< / span > < span class = "n" > hyper< / span > < span class = "o" > -< / span > < span class = "n" > parameters< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s1" > ' classifier__C' < / span > < span class = "p" > :< / span > < span class = "mf" > 100.0< / span > < span class = "p" > ,< / span > < span class = "s1" > ' nbins' < / span > < span class = "p" > :< / span > < span class = "mi" > 32< / span > < span class = "p" > }< / span >
< span class = "n" > MAE< / span > < span class = "o" > =< / span > < span class = "mf" > 0.03102< / span >
2021-11-09 15:50:53 +01:00
< / pre > < / div >
< / div >
< p > The parameter < em > val_split< / em > can alternatively be used to indicate
a validation set (i.e., an instance of < em > LabelledCollection< / em > ) instead
of a proportion. This could be useful if one wants to have control
on the specific data split to be used across different model selection
experiments.< / p >
2023-02-08 19:06:53 +01:00
< / section >
< section id = "targeting-a-classification-oriented-loss" >
< h2 > Targeting a Classification-oriented loss< a class = "headerlink" href = "#targeting-a-classification-oriented-loss" title = "Permalink to this heading" > ¶< / a > < / h2 >
2021-11-09 15:50:53 +01:00
< p > Optimizing a model for quantification could rather be
computationally costly.
In aggregative methods, one could alternatively try to optimize
the classifier’
Although this is theoretically suboptimal, many articles in
quantification literature have opted for this strategy.< / p >
< p > In QuaPy, this is achieved by simply instantiating the
classifier learner as a GridSearchCV from scikit-learn.
The following code illustrates how to do that:< / p >
< div class = "highlight-python notranslate" > < div class = "highlight" > < pre > < span > < / span > < span class = "n" > learner< / span > < span class = "o" > =< / span > < span class = "n" > GridSearchCV< / span > < span class = "p" > (< / span >
< span class = "n" > LogisticRegression< / span > < span class = "p" > (),< / span >
< span class = "n" > param_grid< / span > < span class = "o" > =< / span > < span class = "p" > {< / span > < span class = "s1" > ' C' < / span > < span class = "p" > :< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > logspace< / span > < span class = "p" > (< / span > < span class = "o" > -< / span > < span class = "mi" > 4< / span > < span class = "p" > ,< / span > < span class = "mi" > 5< / span > < span class = "p" > ,< / span > < span class = "mi" > 10< / span > < span class = "p" > ),< / span > < span class = "s1" > ' class_weight' < / span > < span class = "p" > :< / span > < span class = "p" > [< / span > < span class = "s1" > ' balanced' < / span > < span class = "p" > ,< / span > < span class = "kc" > None< / span > < span class = "p" > ]},< / span >
< span class = "n" > cv< / span > < span class = "o" > =< / span > < span class = "mi" > 5< / span > < span class = "p" > )< / span >
2023-02-14 17:00:50 +01:00
< span class = "n" > model< / span > < span class = "o" > =< / span > < span class = "n" > DistributionMatching< / span > < span class = "p" > (< / span > < span class = "n" > learner< / span > < span class = "p" > )< / span > < span class = "o" > .< / span > < span class = "n" > fit< / span > < span class = "p" > (< / span > < span class = "n" > dataset< / span > < span class = "o" > .< / span > < span class = "n" > training< / span > < span class = "p" > )< / span >
2021-11-09 15:50:53 +01:00
< / pre > < / div >
< / div >
2023-02-14 17:00:50 +01:00
< p > However, this is conceptually flawed, since the model should be
optimized for the task at hand (quantification), and not for a surrogate task (classification),
i.e., the model should be requested to deliver low quantification errors, rather
than low classification errors.< / p >
2023-02-08 19:06:53 +01:00
< / section >
< / section >
2021-11-09 15:50:53 +01:00
< div class = "clearer" > < / div >
< / div >
< / div >
< / div >
< div class = "sphinxsidebar" role = "navigation" aria-label = "main navigation" >
< div class = "sphinxsidebarwrapper" >
2023-02-08 19:06:53 +01:00
< div >
< h3 > < a href = "index.html" > Table of Contents< / a > < / h3 >
< ul >
2021-11-09 15:50:53 +01:00
< li > < a class = "reference internal" href = "#" > Model Selection< / a > < ul >
< li > < a class = "reference internal" href = "#targeting-a-quantification-oriented-loss" > Targeting a Quantification-oriented loss< / a > < / li >
< li > < a class = "reference internal" href = "#targeting-a-classification-oriented-loss" > Targeting a Classification-oriented loss< / a > < / li >
< / ul >
< / li >
< / ul >
2023-02-08 19:06:53 +01:00
< / div >
< div >
< h4 > Previous topic< / h4 >
< p class = "topless" > < a href = "Methods.html"
title="previous chapter">Quantification Methods< / a > < / p >
< / div >
< div >
< h4 > Next topic< / h4 >
< p class = "topless" > < a href = "Plotting.html"
title="next chapter">Plotting< / a > < / p >
< / div >
2021-11-09 15:50:53 +01:00
< div role = "note" aria-label = "source link" >
< h3 > This Page< / h3 >
< ul class = "this-page-menu" >
< li > < a href = "_sources/Model-Selection.md.txt"
rel="nofollow">Show Source< / a > < / li >
< / ul >
< / div >
< div id = "searchbox" style = "display: none" role = "search" >
< h3 id = "searchlabel" > Quick search< / h3 >
< div class = "searchformwrapper" >
< form class = "search" action = "search.html" method = "get" >
< input type = "text" name = "q" aria-labelledby = "searchlabel" autocomplete = "off" autocorrect = "off" autocapitalize = "off" spellcheck = "false" / >
< input type = "submit" value = "Go" / >
< / form >
< / div >
< / div >
2023-02-08 19:06:53 +01:00
< script > document . getElementById ( 'searchbox' ) . style . display = "block" < / script >
2021-11-09 15:50:53 +01:00
< / div >
< / div >
< div class = "clearer" > < / div >
< / div >
< div class = "related" role = "navigation" aria-label = "related navigation" >
< h3 > Navigation< / h3 >
< ul >
< li class = "right" style = "margin-right: 10px" >
< a href = "genindex.html" title = "General Index"
>index< / a > < / li >
< li class = "right" >
< a href = "py-modindex.html" title = "Python Module Index"
>modules< / a > |< / li >
2023-02-08 19:06:53 +01:00
< li class = "right" >
< a href = "Plotting.html" title = "Plotting"
>next< / a > |< / li >
< li class = "right" >
< a href = "Methods.html" title = "Quantification Methods"
>previous< / a > |< / li >
< li class = "nav-item nav-item-0" > < a href = "index.html" > QuaPy 0.1.7 documentation< / a > » < / li >
2021-11-09 15:50:53 +01:00
< li class = "nav-item nav-item-this" > < a href = "" > Model Selection< / a > < / li >
< / ul >
< / div >
< div class = "footer" role = "contentinfo" >
© Copyright 2021, Alejandro Moreo.
2023-02-08 19:06:53 +01:00
Created using < a href = "https://www.sphinx-doc.org/" > Sphinx< / a > 5.3.0.
2021-11-09 15:50:53 +01:00
< / div >
< / body >
< / html >
