How can you avoid underfitting in ANN?

Let's imagine a HR SaaS for employee churn prediction🎯. Here's how to address the challenge of underfitting Focus on Quality, not Just Quantity: ✅Curate relevant data: Gather data directly relevant to employee engagement, perf, & potential churn factors, like skills assessments, or perf reviews ✅Prioritize data accuracy: robust data collection & validation processes to min. errors, and missing values. ✅Leverage expert insights: add relevant data points or hidden patterns. 💪Beyond Quantity, Embrace Diversity: ✅Consider data augmentation ✅Incorporate external data By prioritizing data quality, relevance, and diversity, you can: 💡Mitigate underfitting 💡Enhance model generalizability 💡Improve churn prediction accuracy

Reasons for underfitting include: 1-Model simplicity, which may fail to capture data complexities. 2-Inadequate representation of underlying factors by input features. 3-Insufficient training data size. 4-Excessive regularization, hindering the model's ability to capture data patterns. 5-Unscaled features. To mitigate underfitting, consider: 1-Increasing model complexity. 2-Adding more features through feature engineering. 3-Removing noise from the data. 4-Increasing epochs or training duration for improved results.

Underfitting occurs when models are too simplistic, performing poorly on both training and test data, thus displaying high bias and limited ability to generalize. Activation functions like Sigmoid and Tanh suffer from the vanishing gradient problem. While ReLU addresses this issue efficiently ,it may encounter the dying ReLU issue. Leaky ReLU however offers a solution to the dying ReLU issue and is suitable for hidden layers. The Softmax fucntion converts scores to probabilities and is preferred for outer layer for classification tasks. The choice of activation function depends on the problem and network architecture, and experimentation with different functions is recommended to determine the best performance on validation data.

Hyperparameters in artificial neural networks (ANNs) are preset configurations governing the network's structure and behavior, crucial for optimal performance, generalization, and avoiding overfitting. Essential neural network hyperparameters: 1- Learning Rate: Affects optimization step size, impacting convergence speed. 2- Batch Size: Defines samples processed before updates, affecting generalization and training speed. 3- Number of Layers and Units: Determines architecture and complexity. 4- Activation Function: Introduces non-linearities like Sigmoid, ReLU, and Tanh. 5- Weight Initialization: Affects gradient stability during training. 6- Regularization Techniques: Prevent overfitting, including Dropout and L1/L2 regularization.

Early stopping is a regularization technique used in artificial neural networks (ANNs) to prevent overfitting during training. It involves monitoring the model's performance on a validation dataset and halting training when the performance begins to degrade, typically indicated by an increase in validation error. By stopping training early, before the model starts to overfit the training data, early stopping helps improve generalization and ensures the model performs well on unseen data.

To avoid underfitting in ANNs, increase model complexity with more neurons or layers, adjust hyperparameters, consider advanced architectures, and augment training data. Regularly monitor performance for optimal balance between simplicity and generalization.

To avoid underfitting in artificial neural networks (ANNs), increase the model complexity by adding more layers or neurons to capture underlying patterns in the data. Ensure the model is not too simple for the task by adjusting its architecture, increasing the training data, or fine-tuning hyperparameters like learning rate and regularization strength. Additionally, we can consider using more advanced architectures, like convolutional or recurrent layers, depending on the nature of the data. Regularising the model effectively and monitoring the training performance can strike the right balance.