Challenges in Machine Learning:
Inadequate Training Data:
Lack of quality and quantity of data affects ML algorithms.
Noisy, incorrect, and unclean data exhaust ML algorithms.
Data quality issues lead to inaccurate predictions and lower classification accuracy.
Poor Quality of Data:
Noisy, incomplete, and inaccurate data result in low-quality ML results.
Data quality directly impacts the accuracy of classification tasks.
Non-representative Training Data:
Training data must represent new cases accurately.
Non-representative data leads to less accurate predictions and biased models.
Using representative data is crucial for accurate predictions and unbiased models.
Overfitting and Underfitting:
Overfitting:
Occurs when a model learns noise or irrelevant patterns in the training data, leading to poor performance on unseen data.
Results from overly complex models that fit the training data too closely.
Can be mitigated by increasing training data, reducing model complexity, and applying regularization techniques like Lasso or Ridge.
Underfitting:
Occurs when a model is too simplistic to capture the underlying structure of the data.
Typically happens with models that are too simple or trained on insufficient data.
Can be addressed by increasing model complexity, adding relevant features, and training on more data.
Irrelevant Features:
Using irrelevant features leads to garbage results.
Good ML models have a relevant and optimized set of features in the training data.
Offline Learning & Deployment of the Model:
Deploying and managing ML models (MLOps) can be complex and time-consuming.
Requires resources for deployment, monitoring, and updating in production environments.
Choosing the Right Production Requirements:
Critical challenge involves selecting appropriate production requirements.
Factors include data size, processing speed, and security considerations.
Proper consideration ensures optimal performance of ML solutions in production.
Notes:
Inadequate training data impacts ML algorithms' performance, emphasizing the need for quality and quantity.
Overfitting and underfitting highlight the importance of balancing model complexity with data representation.
Irrelevant features and poor data quality significantly affect ML model outcomes.
Deployment challenges and production requirements are crucial for successful ML implementation in real-world scenarios.