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Converted broken markdown links to HTML hyperlinks
Reference table at the end is the only issue still- can't figure out why it spills over the right, tried converting to HTML but that didn't work
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‎guide/14-deep-learning/unsupervised-ml-model-guide.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1.\tGaussian mixture models\n",
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" * [Gaussian mixture models](https://scikit-learn.org/stable/modules/mixture.html)\n",
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"2.\tClustering\n",
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" * [K-means](https://scikit-learn.org/stable/modules/clustering.html#k-means)\n",
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"3.\tNovelty and Outlier Detection\n",
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" * [Novelty Detection](https://scikit-learn.org/stable/modules/outlier_detection.html#novelty-detection)\n",
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" * [Outlier Detection](https://scikit-learn.org/stable/modules/outlier_detection.html#id1)\n",
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" * [Novelty detection with Local Outlier Factor](https://scikit-learn.org/stable/modules/outlier_detection.html#novelty-detection-with-local-outlier-factor)"
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"1. Gaussian Mixture Models\n",
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" * <td><a href=\"https://scikit-learn.org/stable/modules/mixture.html\"><p align=\"left\">Gaussian Mixture Models</p></a></td>\n",
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"2. Clustering\n",
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" * <td><a href=\"https://scikit-learn.org/stable/modules/clustering.html#k-means\"><p align=\"left\">K-means</p></a></td>\n",
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"3. Novelty and Outlier Detection\n",
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" * <td><a href=\"https://scikit-learn.org/stable/modules/outlier_detection.html#novelty-detection\"><p align=\"left\">Novelty Detection</p></a></td>\n",
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" * <td><a href=\"https://scikit-learn.org/stable/modules/outlier_detection.html#id1\"><p align=\"left\">Outlier Detection</p></a></td>\n",
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" * <td><a href=\"https://scikit-learn.org/stable/modules/outlier_detection.html#novelty-detection-with-local-outlier-factor\"><p align=\"left\">Novelty Detection with Local Outlier Factor</p></a></td>"
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### <span style='color:Gray'>**— Initialization of a sample [Gaussian Mixture Model](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html)**</span> "
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"##### <td> Initialization of a sample <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html\">Gaussian Mixture Model</a></td>"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### <span style='color:Gray'>**— Initialization of a sample [K-means clustering Model](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)**</span>"
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"##### <td> Initialization of a sample <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\">K-means Clustering Model</a></td>"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### <span style='color:Gray'>**— Initialization of a sample Anomaly detection [IsolationForest Model](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html)**</span>"
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"##### <td> Initialization of a sample Anomaly detection <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html\">Isolation Forest Model</a></td>"
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]
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### <span style='color:Gray'>**— Initialization of a sample Outlier detection [Elliptic Envelope Model](https://scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope)**</span>"
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"##### <td> Initialization of a sample Outlier detection <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope\">Elliptic Envelope Model</a></td>"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### <span style='color:Gray'>**— Initialization of a sample Unsupervised Novelty detection using [Local Outlier Factor (LOF) model](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor)**</span>"
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"##### <td> Initialization of a sample Unsupervised Novelty detection <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor\">Local Outlier Factor (LOF) Model</a></td>"
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"cell_type": "markdown",
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"metadata": {},
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"##### <span style='color:Gray'>**— Initialization of a sample Unsupervised Outlier and Novelty detection using [One-Class SVM model](https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html)**</span>"
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"##### <td> Initialization of a sample Unsupervised Outlier and Novelty detection using <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html\">One-Class SVM Model</a></td>"
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<span style='color:Gray'>**[Gaussian Mixture Model](https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html)**</span> \n",
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"<span style='color:Gray'><a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html\">Gaussian Mixture Model</a></span> \n",
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"\n",
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"`predict_probability(X)` method predict posterior probability of each component given the data, thereby give probability of each sample for belonging to a certain cluster for Gaussian mixture modeling.\n",
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"\n",
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"<span style='color:Gray'>**[K-means clustering Model](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html)**</span>\n",
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"<span style='color:Gray'><a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html\">K-means Clustering Model</a></span>\n",
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"\n",
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"`score(X, y=None, sample_weight=None)` function returns the opposite of inertia of the fit model, hence the greater the value better is the clustering or lowest inertia returns the best clusters.\n",
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"\n",
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"<span style='color:Gray'>**[IsolationForest Model](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html)**</span>\n",
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"<span style='color:Gray'><a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html\">Isolation Forest Model</a></span>\n",
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"\n",
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"`decision_function(X)` returns average anomaly score of X of the base classifiers. The anomaly score of an input sample is computed as the mean anomaly score of the trees in the forest. This function other than identifying anomaly could also be used understanding explainability of predictors causing the anomaly, and as well selecting anomlay based on upper and lower bound quantile.\n",
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"\n",
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"\n",
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"<span style='color:Gray'>**[One-Class SVM model](https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html)**</span>\n",
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"<span style='color:Gray'><a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html\">One-Class SVM Model</a></span>\n",
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"\n",
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"`decision_function(X)` Signed distance to the separating hyperplane. Signed distance is positive for an inlier and negative for an outlier.\n",
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"\n",
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"<span style='color:Gray'>**[Local Outlier Factor (LOF) model](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor)**</span>\n",
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"<span style='color:Gray'><a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html#sklearn.neighbors.LocalOutlierFactor\">Local Outlier Factor (LOF) Model</a></span>\n",
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"\n",
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"\n",
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"`kneighbors(X=None, n_neighbors=None, return_distance=True)` function finds the K-neighbors of a point, and returns indices of and distances to the neighbors of each point. Using the estimated distance for each sample point it decides the anomaly samples. This is a density-based outliers detection method, based on the its distance from surrounding neighbours, compared to other points & distance of their neighbors.\n",
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"\n",
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"<span style='color:Gray'>**[Elliptic Envelope Model](https://scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope)**</span>\n",
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"<span style='color:Gray'><a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.covariance.EllipticEnvelope.html#sklearn.covariance.EllipticEnvelope\">Elliptic Envelope Model</a></span>\n",
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"\n",
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"`mahalanobis(X)` function computes the squared Mahalanobis distances of given observations."
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"| -| - |-|\n",
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"| MLModel| MLModel from arcgis.learn module |https://developers.arcgis.com/python/api-reference/arcgis.learn.toc.html#mlmodel|\n",
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"| scikit-learn unsupervised models| Unsupervised models implemented from scikit learn |https://scikit-learn.org/stable/unsupervised_learning.html|\n",
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"| Gaussian mixture model| Gaussian mixture model from scikit learn |https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html|| \n",
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"| Gaussian mixture model| Gaussian mixture model from scikit learn |https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html|\n",
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"| Clustering algorithm| Clustering models from scikit learn |https://scikit-learn.org/stable/modules/clustering.html|\n",
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"| K-means Clustering| K-means clustering model |https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html|\n",
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"| Silhouette analysis| Silhouette analysis for optimizing clusters |https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html|\n",

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