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The IEstimator<TTransformer> to predict a target using a linear multiclass classifier model trained with a coordinate descent method. Depending on the used loss function, the trained model can be, for example, maximum entropy classifier or multi-cl
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After watching all the videos of the famous Standford's CS231n course that took place in 2017, i decided to take summary of the whole course to help me to remember and to anyone who would like to know about it. I've skipped some contents in some lectures as it wasn't important to ...
multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=runs\train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1...
# Split data into training and testing sets X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42) # Standardize features by removing the mean and scaling to unit variance scaler=StandardScaler() X_train_scaled=scaler.fit_transform(X_train) ...
# Split data into training and testing sets X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.3,random_state=42)# Standardize features by removing the mean and scaling to unit variance scaler=StandardScaler()X_train_scaled=scaler.fit_transform(X_train)X_test_scaled=scaler.tr...
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