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5.0/404.html

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@@ -2071,11 +2094,12 @@ <h3 id="sklearncvexperiment">SklearnCvExperiment</h3>
 <p>Cross-validation based hyperparameter optimization for scikit-learn estimators.</p>
 <p><strong>Parameters:</strong>
 - <strong>estimator</strong>: Scikit-learn estimator to optimize
-- <strong>param_grid</strong>: Dictionary defining the search space
-- <strong>X, y</strong>: Training data
-- <strong>cv</strong>: Cross-validation strategy (int or CV object)
-- <strong>scoring</strong>: Scoring function or string
-- <strong>n_jobs</strong>: Number of parallel jobs for cross-validation</p>
+- <strong>X, y</strong>: Training data arrays
+- <strong>cv</strong>: Cross-validation strategy (int or CV object). If <code>None</code>, defaults to <code>KFold(n_splits=3, shuffle=True)</code>.
+- <strong>scoring</strong>: Scoring function or string. If <code>None</code>, uses the estimator’s default <code>score</code>.</p>
+<p>Notes:
+- Parallelization is controlled by the optimizer via <code>backend</code> and <code>backend_params</code> (e.g., <code>GridSearchSk</code>, <code>RandomSearchSk</code>), not by <code>SklearnCvExperiment</code>.
+- Define your search space (e.g., grids, distributions) on the optimizer; the experiment only encapsulates data, estimator, CV, and scoring.</p>
 <h3 id="optcv-scikit-learn-compatible-optimizer">OptCV - Scikit-learn Compatible Optimizer</h3>
 <p>For users who prefer the familiar scikit-learn interface, <code>OptCV</code> provides a drop-in replacement for <code>GridSearchCV</code> and <code>RandomizedSearchCV</code>.</p>
 <p><strong>Key Features:</strong>
@@ -2107,7 +2131,7 @@ <h3 id="performance-optimization">Performance Optimization</h3>
 <ol>
 <li><strong>Caching</strong>: Cache expensive computations when possible</li>
 <li><strong>Early stopping</strong>: Return early for obviously poor parameters</li>
-<li><strong>Parallel evaluation</strong>: Use n_jobs for cross-validation</li>
+<li><strong>Parallel evaluation</strong>: Use optimizer <code>backend</code>/<code>backend_params</code> for CV parallelization</li>
 <li><strong>Memory management</strong>: Clean up resources in long-running experiments</li>
 </ol>
 <h3 id="integration-guidelines">Integration Guidelines</h3>

5.0/api/index.html

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@@ -2387,10 +2410,10 @@ <h1 id="optimization-algorithms">Optimization Algorithms</h1>
 <h2 id="backend-overview">Backend Overview</h2>
 <h3 id="gradient-free-optimizers-gfo-backend">Gradient-Free-Optimizers (GFO) Backend</h3>
 <p>The GFO backend provides 16 optimization algorithms with consistent interfaces and comprehensive parameter control.</p>
-<p><strong>Import from:</strong> <code>hyperactive.opt.gfo</code></p>
+<p><strong>Import from:</strong> <code>hyperactive.opt.gfo</code> (also re-exported in <code>hyperactive.opt</code>)</p>
 <h3 id="optuna-backend">Optuna Backend</h3>
 <p>The Optuna backend provides 8 modern optimization algorithms with advanced features like multi-objective optimization.</p>
-<p><strong>Import from:</strong> <code>hyperactive.opt.optuna</code></p>
+<p><strong>Import from:</strong> <code>hyperactive.opt.optuna</code> (also re-exported in <code>hyperactive.opt</code>)</p>
 <h3 id="scikit-learn-backend">Scikit-learn Backend</h3>
 <p>Direct integration with scikit-learn's optimization tools for familiar workflows.</p>
 <p><strong>Import from:</strong> <code>hyperactive.opt.gridsearch</code> and <code>hyperactive.opt</code></p>

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@@ -2086,9 +2109,7 @@ <h2 id="introduction">Introduction</h2>
 <p>The Ackley function is a widely used multimodal benchmark function for testing optimization algorithms. It features many local minima and a single global minimum, making it an excellent test for an algorithm's ability to avoid getting trapped in local optima.</p>
 <h2 id="mathematical-definition">Mathematical Definition</h2>
 <p>The Ackley function is defined as:</p>
-<div class="arithmatex">\[f(x) = -a \exp\left(-b\sqrt{rac{1}{d}\sum_{i=1}^d x_i^2}
-ight) - \exp\left(rac{1}{d}\sum_{i=1}^d \cos(c x_i)
-ight) + a + e\]</div>
+<div class="arithmatex">\[f(x) = -a \exp\left(-b\sqrt{rac{1}{d}\sum_{i=1}^d x_i^2}\right) - \exp\left(rac{1}{d}\sum_{i=1}^d \cos(c x_i)\right) + a + e\]</div>
 <p>Where:
 - <span class="arithmatex">\(a = 20\)</span> (amplitude of exponential term)
 - <span class="arithmatex">\(b = 0.2\)</span> (exponential decay factor)<br />

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