These are primary metrics to evaluate the quality of a classification model. Let TP,
FP, TN, and FN denote true positive, false positive, true negative, and false negative. The classification Accuracy and Error Rate are defined in the image below.

Accuracy and Error Rate

These are also primary metrics and are more often used than accuracy or error
rate for imbalanced test sets, e.g., the majority of the test samples have one class label. Precision and recall for binary classification are defined as below. The F1 score is the harmonic mean of the precision and recall, as in Eq. 3. An F1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.

Precision / Recall / F1 score

The exact match metric is a popular metric for question-answering systems, which
measures the percentage of predictions that match any one of the ground truth answers exactly. EM is one of the main metrics used for SQuAD.

Exact Match (EM)

MRR is often used to evaluate the performance of ranking algorithms in NLP
tasks such as query-document ranking and QA. MRR is defined below, where 𝑄 is a set of all possible answers, and 𝑟𝑎𝑛𝑘(𝑖) is the ranking position of the ground-truth answer.


Mean Reciprocal Rank (MRR)

5 main methods: Accuracy and Error Rate; Precision / Recall / F1 score; Exact Match (EM); Mean Reciprocal Rank (MRR)

Saint Olaf College

A helpful website that introduces neural networks:
https://missinglink.ai/guides/neural-network-concepts/

Neural Network Reference

https://techcrunch.com/2017/04/13/neural-networks-made-easy/

Neural network made easy

Pros: They can effectively capture complex features given enough data and computation.
Cons: The training time, data and customization is significant if the model is large and complex. Data need to be carefully preprocessed to be used as input. 

Pros and Cons of Neural Networks

Depends greatly on the nature of the target task and domain, the availability of in-domain labels, the latency and capacity constraints of the applications, and so on. Has 5 steps.

How to choose the best neural network model for my task.

+ New Datasets for More Challenging Tasks
+ Modeling Commonsense Knowledge
+ Interpretable DL Models
+ Memory Efficient Models
+ Few-Shot and Zero-Shot Learning

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