Identifying Anomalies through Unsupervised Machine Learning Approaches
In the realm of data analysis, outliers can significantly impact modeling, especially in Linear Regression. Fortunately, Scikit-Learn provides several methods to identify these anomalous data points, including the Local Outlier Factor (LOF) and Gaussian Mixture Model (GMM).
Local Outlier Factor (LOF)
To employ the LOF algorithm, begin by importing the module:
Your dataset should be prepared in a numerical array or DataFrame format. Next, instantiate the LOF model, setting parameters such as the number of neighbours to consider () and the expected proportion of outliers ().
Fit the model and predict the outlier labels using on your data .
The output labels are for outliers and for inliers. If you need the LOF scores, these can be accessed with the attribute.
To flag outliers, set a threshold based on the LOF scores. Points with scores significantly above 1 (e.g., >1.5) can be considered outliers.
Gaussian Mixture Model (GMM)
To utilize the GMM algorithm, start by importing the module:
Prepare the dataset as before, and then fit the GMM model, choosing the number of components (clusters) to fit.
Compute the probabilities of each point under the model using .
Identify outliers based on these probabilities, setting a threshold to flag points with very low probabilities.
Comparing the Methods
| Method | Detection Mechanism | |-----------------------|----------------------------------------------------------------| | LOF | Compares local density of a point to its neighbours; points with low local density are outliers. | | GMM | Fits Gaussian clusters; points with low likelihood under all clusters are outliers. |
Example Code Snippets
```python from sklearn.neighbors import LocalOutlierFactor from sklearn.mixture import GaussianMixture
lof = LocalOutlierFactor(n_neighbors=20, contamination=0.05) outlier_labels = lof.fit_predict(X) lof_scores = -lof.negative_outlier_factor_
gmm = GaussianMixture(n_components=3) gmm.fit(X) log_prob = gmm.score_samples(X) threshold = np.percentile(log_prob, 5) gmm_outliers = log_prob < threshold ```
These steps outline how to use LOF and GMM in Scikit-Learn for outlier detection. For more information, consult the book "Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron.
For a deeper understanding of data cleaning and exploration techniques, including outlier detection, refer to "Data Cleaning and Exploration with Machine Learning" by Michael Walker. The full code for using the LOF and GMM algorithms for outlier detection can be found on GitHub.
Additionally, the Isolation Forest algorithm is another method for finding outliers in data. When applying the LOF algorithm to the dataset with a contamination rate of 9%, it can be used to find outliers that were intentionally added to the dataset.
In the context of data analysis, both Local Outlier Factor (LOF) and Gaussian Mixture Model (GMM) can be utilized as technology-based methods for identifying outliers in medical-conditions data, thanks to data-and-cloud-computing tools like Scikit-Learn. LOF compares the local density of a point to its neighbors, marking points with low local density as outliers, while GMM fits Gaussian clusters, flagging points with low likelihood under all clusters as outliers.
