removed extraneous parameters label from input feats in docs

docs/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 4d115e8219837f5e7663068621637d0a
+config: 30c16f527d03e2f32b3ab00067a9aa8f
 tags: 645f666f9bcd5a90fca523b33c5a78b7

docs/_sources/usage_guide.rst.txt

@@ -168,8 +168,6 @@ Input Parameters
 
    A class for building, tuning, and evaluating machine learning models, supporting classification, regression, and multi-label tasks.
 
-   **Parameters**
-
    :param name: A name for the model, useful for identifying the model in outputs and logs.
    :type name: str
    :param estimator_name: The prefix for the estimator used in the pipeline. This is used in parameter tuning (e.g., estimator_name + ``__param_name``).

docs/_static/searchtools.js

@@ -178,7 +178,7 @@ const Search = {
 
   htmlToText: (htmlString, anchor) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    for (const removalQuery of [".headerlink", "script", "style"]) {
+    for (const removalQuery of [".headerlinks", "script", "style"]) {
       htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
     }
     if (anchor) {
@@ -328,14 +328,13 @@ const Search = {
     for (const [title, foundTitles] of Object.entries(allTitles)) {
       if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
-          const score = Math.round(Scorer.title * queryLower.length / title.length);
-          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          let score = Math.round(100 * queryLower.length / title.length)
           normalResults.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
             null,
-            score + boost,
+            score,
             filenames[file],
           ]);
         }

docs/usage_guide.html

@@ -274,7 +274,6 @@ <h1>Input Parameters<a class="headerlink" href="#input-parameters" title="Link t
 <dt class="sig sig-object py" id="Model">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">Model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator_name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">estimator</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibrate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kfold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">imbalance_sampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">validation_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stratify_y</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stratify_cols</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">drop_strat_feat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scoring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">['roc_auc']</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_splits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">random_state</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_jobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">display</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">feature_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">randomized_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_iter</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipeline</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pipeline_steps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">xgboost_early</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">selectKBest</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'classification'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_labels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">multi_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">calibration_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sigmoid'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">custom_scorer</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#Model" title="Link to this definition"></a></dt>
 <dd><p>A class for building, tuning, and evaluating machine learning models, supporting classification, regression, and multi-label tasks.</p>
-<p><strong>Parameters</strong></p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">

source/usage_guide.rst

@@ -168,8 +168,6 @@ Input Parameters
 
    A class for building, tuning, and evaluating machine learning models, supporting classification, regression, and multi-label tasks.
 
-   **Parameters**
-
    :param name: A name for the model, useful for identifying the model in outputs and logs.
    :type name: str
    :param estimator_name: The prefix for the estimator used in the pipeline. This is used in parameter tuning (e.g., estimator_name + ``__param_name``).