04 - API Summary.ipynb

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   "source": [
    "# A recap on Scikit-learn's estimator interface\n"
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    "``X`` : data, 2d numpy array or scipy sparse matrix of shape (n_samples, n_features)\n",
    "\n",
    "``y`` : targets, 1d numpy array of shape (n_samples,)"
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    "## Methods"
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    "<table>\n",
    "<tr style=\"border:None; font-size:20px; padding:10px;\"><th colspan=2>``model.fit(X_train, [y_train])``</td></tr>\n",
    "<tr style=\"border:None; font-size:20px; padding:10px;\"><th>``model.predict(X_test)``</th><th>``model.transform(X_test)``</th></tr>\n",
    "<tr style=\"border:None; font-size:20px; padding:10px;\"><td>Classification</td><td>Preprocessing</td></tr>\n",
    "<tr style=\"border:None; font-size:20px; padding:10px;\"><td>Regression</td><td>Dimensionality Reduction</td></tr>\n",
    "<tr style=\"border:None; font-size:20px; padding:10px;\"><td>Clustering</td><td>Feature Extraction</td></tr>\n",
    "<tr style=\"border:None; font-size:20px; padding:10px;\"><td>&nbsp;</td><td>Feature selection</td></tr>\n",
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    "## Efficient alternatives, methods for models that don't generalize\n",
    "``model.fit_predict(X)`` (clustering)\n",
    "\n",
    "``model.fit_transform(X)`` (manifold learning)"
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    "###Additional methods\n",
    "__Model evaluation__ : ``score(X, [y])``\n",
    "\n",
    "__Uncertainties from Classifiers__: ``decision_function(X)`` and ``predict_proba(X)``."
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    "##Attributes\n",
    "__Classifiers__: ``classes_``\n",
    "\n",
    "__Clustering__: ``labels_``\n",
    "\n",
    "__Manifold Learning__: ``embedding_``\n",
    "\n",
    "__Linear models__: ``coef_``\n",
    "\n",
    "__Linear Decompositions__: ``components_``"
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