Introduction

In the realm of deep learning and artificial intelligence, evaluating the performance of your models accurately is pivotal. Keras, as a user-friendly and highly modular deep learning library running on top of TensorFlow, facilitates this process efficiently. One of the primary metrics used to evaluate the model's performance is accuracy. This article provides a detailed examination of how to calculate and analyze the accuracy of a model using Keras.

What is Accuracy?

Accuracy is a metric used to measure the proportion of correctly predicted instances in a dataset. It is calculated as follows:

$$\text{Accuracy} = \frac{\text{Number of Correct Predictions}}{\text{Total Number of Predictions}}$$

In the context of classification problems, accuracy is the ratio of the number of correct predictions to the total number of input samples. While it is easy to interpret, accuracy might not always be the best metric, especially when dealing with imbalanced classes.

Setting Up Keras

Before getting started, ensure you have the Keras library installed. If not, install it using pip:

• We generated a dataset with 1000 samples, each having 20 features.
• We created a simple feedforward neural network (Sequential ).
• It consists of an input layer with 64 nodes, a hidden layer with 32 nodes, and an output layer of 1 node with a sigmoid activation for binary classification.
• We compiled the model with the adam optimizer, binary_crossentropy loss function, and specified accuracy as a metric.
• The model is trained over 50 epochs with a batch size of 10.
• 20% of the data is used for validation purposes.
• Imbalanced Data: Accuracy can be misleading when classes in the dataset are unbalanced. In such cases, other metrics like precision, recall, F1-score, or area under the ROC curve (AUC-ROC) might be more informative.
• Epoch and Batch Size: Proper selection of epochs and batch size is important for achieving optimal accuracy. Too few epochs might result in underfitting, while too many might cause overfitting.
• Early Stopping: Implementing callbacks like early stopping can prevent overfitting by halting training when a monitored metric has stopped improving.