Understanding Underfitting in Machine Learning
Machine Learning's concept of underfitting involves a model that cannot aptly model the training data nor generalize to new data. An unfit model proves inadequate, as demonstrated by subpar performance on the training data. Underfitting isn't typically a focal discussion point in Machine Learning because it can be easily identified with the right performance metric.
Underfitting describes a situation with high bias and low variance, where a machine learning model lacks the complexity to adequately grasp the associations between a dataset's characteristics and a target variable. An unfit model tends to yield incorrect outputs for data it's not been trained on and even performs poorly with the training data.
Examples of Underfitting
A common instance would be a model claiming that a persistent rise in marketing expenditure will always lead to increased sales, without considering the saturation effect. If a business relies on such a model to determine its marketing budget, they will inevitably overspend.
Strategies to Overcome Underfitting
Enhancing Model Complexity
- Enhance the model's complexity: It's plausible that your model is underfitting since it's too simplistic to identify patterns. A transition from a linear to a non-linear model or including hidden layers to your neural network model may rectify underfitting.
Incorporating New Features
- Incorporate new features: If the training data is too basic, it may result in underfitting. Including additional features that reveal relevant patterns and generate accurate predictions can aid in overcoming underfitting.
- Reduce regularization: The algorithms you choose include default regularization settings to avoid overfitting. However, occasionally, these can prevent learning from occurring. Reducing these settings can be beneficial.
Impressively, these underfitting solutions don't involve adding new data. If your data doesn't possess the necessary features that enable your model to detect patterns, amplifying your training set will barely improve the algorithm.
Ironically, many engineers mistakenly believe that increasing data will solve any issue. This flawed approach can jeopardize a project, given that it can lead to expensive and time-consuming data collection.
The Art of Balancing a Model
Identifying and Addressing Fit Issues
The skill to identify and tackle underfitting/overfitting is an essential part of model development. Besides the methods mentioned above, a myriad of other techniques exists that can effectively address such issues.
The Ideal Model Balance
Ultimately, the goal is achieving a balance between a model that underfits and overfits. This involves looking at how the system performs as it learns from the training data over time. Ideally, a model should exhibit proficiency on both the training dataset and the unseen test dataset before the error on the test dataset rises. While this is an ideal objective, it's equally challenging to attain in practice.