What are some metrics for evaluating the interpretability of machine learning models?

Aug 1, 2019 ... ... CNN), which is kind of deep learning. Mask R-CNN technique is a recently proposed algorithm not only for object detection and object ... Dec 7, 2017 ... While people usually tend to simply resize any image into a square while training a CNN (for example, resnet takes a 224x224 square image), that looks ugly to ... Apr 12, 2023 ... This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state ( ... A convolutional neural network (CNN or ConvNet) is a network architecture for deep learning that learns directly from data. Neural networks are a subset of machine learning, and they are at the heart of deep learning algorithms. ... With each layer, the CNN increases in its complexity, ... Nov 28, 2022 ... In this study, different deep CNN models with multiple machine learning classifiers are investigated for monkeypox disease diagnosis using ... May 12, 2022 ... Comments94 · Day 1- Live Deep Learning Community Session · Day 3- Optimizers And ANN Implementation| Live Deep Learning Community Session. Feb 10, 2020 ... The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models ... ... intelligence must solve. One of the solutions is to apply deep learning and computer vision. Convolutional Neural Networks' (CNN) deep architecture ... Feb 18, 2021 ... The proposed model is called parallel LSTM-CNN Network or PLCNet. Two real-world data sets, namely “hourly load consumption of Malaysia ” as ...