Machine learning models for gully erosion susceptibility assessment in the Tensift catchment, Haouz Plain, Morocco for sustainable development To gauge the performance of the ML models, an array of metrics including specificity, precision, sensitivity, and accuracy were employed. The study ... Y Bamm...
Deep learning algorithms, in particular, can uncover relations in the data on a scale that would be impossible by inspection alone, owing to their ability to capture complex dependencies with minimal prior assumptions20. Although deep learning models can produce highly accurate phenotypic predictions12,...
One of the main concerns about fairness in machine learning (ML) is that, in order to achieve it, one may have to trade off some accuracy. To overcome this
Restricted Boltzmann Machines (RBMs) offer a versatile architecture for unsupervised machine learning that can in principle approximate any target probability distribution with arbitrary accuracy. However, the RBM model is usually not directly accessible due to its computational complexity, and Markov-chain ...
Statistical Parameters:We also consider the p-values, information values and other statistical metrics to select right features. PCA:It helps to represent training data into lower dimensional spaces, but still characterize the inherent relationships in the data. It is a type of dimensionality reduction...
In this study, seven of the most used accuracy metrics in machine learning were summarized, and both their advantages and disadvantages were studied. To achieve this, the acoustic emission data of damage locations were collected from a pile hit test. A backpropagation artificial neural network ...
The application of artificial intelligence (AI) in diagnostic imaging has gained significant interest in recent years, particularly in lung cancer detection. This systematic review aims to assess the accuracy of machine learning (ML) AI algorithms in lung cancer detection, identify the ML architectures...
for layer in resnet50.layers: layer.trainable = True filepath=”weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5″ #Compile the model model.compile(optimizer=Adam(lr=0.000001), loss=’categorical_crossentropy’, metrics=[‘accuracy’]) ...
FutureWarning) 利用sklearn可以得到混淆矩阵 In [48]: from sklearn.metrics import confusion_matrix confusion_matrix(y_train_5,y_train_pred) Out[48]: array([[53562, 1017], [ 1197, 4224]]) 混淆矩阵中的每一行表示一个实际的类, 而每一列表示一个预测的类。该矩阵的第一行认为“非 5”(反例)...
For the datasets that we analyzed, we identified the decision tree model or their variants to be the best classifiers with excellent performance on the metrics used. In particular, ensemble learning algorithms such as Random Forest are well suited for large datasets such as that of amperometry ...