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Jun 13, 2024 · Steps. loop through all the data points and compute the Z-score using the formula (Xi-mean)/std. define a threshold value of 3 and mark the datapoints whose absolute value of Z-score is greater than the threshold as outliers. import numpy as np. outliers = [] def detect_outliers_zscore ( data ): thres = 3. mean = np.mean(data)
Jun 27, 2024 · Explanation of Terms: ROC is the Receiver Operating Characteristics curve for a machine learning model that is formed when one plats the the TPR (True Positive Rate) against the FPR (False Positive Rate) for all probability predictions produced by the model. From that curve one can calculate the AUC (Area Under the Curve).
4 days ago · K-fold cross validation in machine learning cross-validation is a powerful technique for evaluating predictive models in data science. It involves splitting the dataset into k subsets or folds, where each fold is used as the validation set in turn while the remaining k-1 folds are used for training. This process is repeated k times, and ...
Jun 25, 2024 · In math, Support Vector Machine (SVM) is a supervised machine learning algorithm used for binary classification. It seeks to find an optimal hyperplane that best separates two classes in a dataset. Mathematically, SVM involves maximizing the margin between the classes while minimizing the norm of the weight vector, subject to the constraint that each data point is correctly classified within a specified margin.
Jun 13, 2024 · This makes it difficult to determine whether his happiness is more influenced by eating chips or watching television, exemplifying the multicollinearity problem. In the context of machine learning, multicollinearity, marked by a correlation coefficient of +1.0 or -1.0 between variables, can lead to less dependable statistical conclusions.
Jun 10, 2024 · ROC Curve & AUC: Visualizes the trade-off between true positive rate and false positive rate at various thresholds. Higher AUC means a better model overall. PR-Curve: Similar to ROC, but focuses on the trade-off between precision and recall. Helpful for imbalanced datasets. Matthews Correlation Coefficient
6 days ago · AUC_roc: Area Under the Curve: The AUC_roc estimates the area under the receiving operator characteristic curve following the trapezoid approach. It is bounded between 0 and 1. The closet to 1 the better. AUC_roc = 0.5 means the models predictions are the same than a random classifier. \(AUC_{roc} = precision+npv-1 = PPV + NPV -1\) 27: p4: P4 ...
