The records are stored in a text file named “medical_records.data”. Each row corresponds to a patient record. The diagnosis is the attribute predicted. In this dataset, the diagnosis is the second field and is either B (benign) or M (malignant). There are 32 attributes in total (ID, diagnosis, and 30 real-valued input features
1. ID number
2. Diagnosis (M = malignant, B = benign) 3-32
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry j) fractal dimension ("coastline approximation" - 1) The mean, standard error, and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius. All feature values are recorded with four significant digits.
Preprocessing Complete the function prepare_dataset that loads the data records from the text file, and converts the information to numpy arrays.
NEW YEAR OFFER 50% OFF !!! Order Now
NEW YEAR OFFER 50% OFF !!
Lets take Best opinion from our Expert Tutors Today! Order Now
*Disclaimer: The reference papers provided by QuickAssignmentHelp.net serve as model papers for students and not to be submitted as it is. These papers are intended to used for research and reference purposes only.
Copyright © 2023 QuickAssignmentHelp.net All right reserved.