How to Calibrate Probabilities for Imbalanced Classification
Many machine learning models are capable of predicting a probability or probability-like scores for class membership. Probabilities provide a required level of granularity for evaluating and comparing...
View ArticleDevelop a Model for the Imbalanced Classification of Good and Bad Credit
Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank...
View ArticleImbalanced Classification Model to Detect Mammography Microcalcifications
Cancer detection is a popular example of an imbalanced classification problem because there are often significantly more cases of non-cancer than actual cancer. A standard imbalanced classification...
View ArticlePredictive Model for the Phoneme Imbalanced Classification Dataset
Many binary classification tasks do not have an equal number of examples from each class, e.g. the class distribution is skewed or imbalanced. Nevertheless, accuracy is equally important in both...
View ArticleImbalanced Classification with the Adult Income Dataset
Many binary classification tasks do not have an equal number of examples from each class, e.g. the class distribution is skewed or imbalanced. A popular example is the adult income dataset that...
View ArticleStep-By-Step Framework for Imbalanced Classification Projects
Classification predictive modeling problems involve predicting a class label for a given set of inputs. It is a challenging problem in general, especially if little is known about the dataset, as there...
View ArticleImbalanced Classification with the Fraudulent Credit Card Transactions Dataset
Fraud is a major problem for credit card companies, both because of the large volume of transactions that are completed each day and because many fraudulent transactions look a lot like normal...
View ArticleImbalanced Multiclass Classification with the Glass Identification Dataset
Multiclass classification problems are those where a label must be predicted, but there are more than two labels that may be predicted. These are challenging predictive modeling problems because a...
View ArticleImbalanced Multiclass Classification with the E.coli Dataset
Multiclass classification problems are those where a label must be predicted, but there are more than two labels that may be predicted. These are challenging predictive modeling problems because a...
View ArticleMulti-Class Imbalanced Classification
Imbalanced classification are those prediction tasks where the distribution of examples across class labels is not equal. Most imbalanced classification examples focus on binary classification tasks,...
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