XGBClassifier
This predictive model, employing the powerful XGBoost (Extreme Gradient Boosting) algorithm, is based on the UCI ML Breast Cancer Wisconsin (Diagnostic) dataset.
This predictive model, employing the powerful XGBoost (ExtremeGradient Boosting) algorithm, is based on the UCI ML Breast Cancer Wisconsin(Diagnostic) dataset. The dataset comprises features computed from a digitizedimage of a fine needle aspirate (FNA) of a breast mass, describingcharacteristics of the cell nuclei present in the image.
To try this model, select Model 1 in the Privasea client and copy your feature vector as the X vector. The client will then encrypt thisvector on your local machine using your locally stored client key, and theencrypted data will be transmitted to the Privanetix nodes. Subsequentinference operations will take place in the encrypted domain on the Privanetixnodes, and the encrypted result will be sent back to your client. Upon arrival,the result will be decrypted using your locally stored client key. Thedecrypted result will yield a two-dimensional vector representing thelikelihood of the associated tags. For instance, a result like "011,0.89" signifies a classification as true. You can compare it with theprovided output in the tables.
Features vector：
[
mean radius
mean texture
mean perimeter
mean area
mean smoothness
mean compactness
mean concavity
mean concave points
mean symmetry
mean fractal dimension
radius error
texture error
perimeter error
area error
smoothness error
compactness error
concavity error
concave points error
symmetry error
fractal dimension error
worst radius
worst texture
worst perimeter
worst area
worst smoothness
worst compactness
worst concavity
worst concave points
worst symmetry
worst fractal dimension
]
Output：
[
0, false
1, true
]