![]() The data was downloaded from the UC Irvine Machine Learning Repository. Computer-derived nuclear features distinguish malignant from benign breast cytology. Computerized breast cancer diagnosis and prognosis from fine needle aspirates. Analytical and Quantitative Cytology and Histology, Vol. ![]() ![]() Image analysis and machine learning applied to breast cancer diagnosis and prognosis. Machine learning techniques to diagnose breast cancer from fine-needle aspirates. Breast cancer diagnosis and prognosis via linear programming. IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology, volume 1905, pages 861-870, San Jose, CA, 1993. Nuclear feature extraction for breast tumor diagnosis. The data I am going to use to explore feature selection methods is the Breast Cancer Wisconsin (Diagnostic) Dataset: And once you’ve got a feel for your data, investing the time and effort to compare different feature selection methods (or engineered features), model parameters and - finally - different machine learning algorithms can make a big difference!īreast Cancer Wisconsin (Diagnostic) Dataset With machine learning, there is no “one size fits all”! It is always worthwhile to take a good hard look at your data, get acquainted with its quirks and properties before you even think about models and algorithms. But even this small example shows how different features and parameters can influence your predictions. My conclusions are of course not to be generalized to any ol’ data you are working with: There are many more feature selection methods and I am only looking at a limited number of datasets and only at their influence on Random Forest models.
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