Baseline Models

What are Baseline Models?

Baseline models are basic models that are used as a foundation for more complicated models.

  • They are a benchmark against which more complex models may be evaluated.

In machine learning and other forms of predictive modeling, baseline models are often used to set a bar or minimum degree of accuracy that must be surpassed before moving on to more sophisticated models.

Simple techniques, such as linear regression, decision trees, or closest-neighbor approaches, may be used to build baseline models. The complexity of the model and the method used are both factors that must be considered in relation to the data and the situation at hand.

In most cases, baseline models are trained on a small sample of the data and tested on a larger validation set. Accuracy, precision, recall, F1-score, and other applicable measures may be used to evaluate a baseline model’s performance. However, this will vary from issue domain to problem domain.

Baseline models in classification

For classification tasks, a baseline model is a basic, typically bare-bones model that may be used as a foundation for more advanced models. The purpose of a model baseline is to lay down a floor of precision that must be surpassed before more advanced models may be considered.

The majority class classifier, which consistently predicts the most frequent class in the training data, is a popular baseline method for classification problems. In circumstances when there is an imbalance between the classes, this seemingly simple paradigm may be rather useful.

The random classifier is an alternative baseline model for classification that makes indistinguishable predictions about the class labels. Any alternative classification model should do better than this one; however, it serves as a benchmark for comparison.

Baseline models for classification problems also include basic rule-based models that are easy to perceive and understand, such as decision trees or logistic regression. As we already mentioned, these models may also be used as standards against which more advanced models, such as neural networks or ensemble techniques, can be evaluated.

The facts and the task at hand should guide the selection of a baseline method. Simple, easily implemented baseline models that may serve as a solid foundation for more intricate models are ideal. Accuracy, precision, recall, F1-score, and other assessment measures are used to determine how well they perform on the validation set after being trained on a smaller portion of the data.

Importance of Baseline Models

  • Overfitting– They aid in preventing overfitting by revealing when more intricate models are unable to adequately generalize and perform on fresh data. This may be useful for avoiding the time and resources invested in models that aren’t likely to function well with updated data.
  • They serve as a basis for further model creation– A baseline model is a ground zero, uncomplicated model that may be used as a starting point for more intricate models. They might help provide a baseline against which the accuracy or performance of more sophisticated models can be contested.
  • Symplfy the model development process– Baseline models aid in streamlining the process of developing new models since they are often less complicated and use less computer power. During the period spent developing the model, this may be a significant time and money saver.
  • Identify data quality issues– Baseline models may aid in the detection of data quality problems such as missing values, outliers, and class imbalance. The model may be refined to account for these details in later iterations.
  • A benchmark for model efficiency– The baseline model in machine learning serves as a yardstick against which more complicated models may be judged. They may be used to evaluate the value of investing in more intricate models by comparing the results of several models.

Baseline Models

  • Reduce Risk
  • Simplify Compliance
  • Gain Visibility
  • Version Comparison