What is baseline

Baseline in Machine Learning is the starting point from which all further progress is measured. It is used to measure the performance of a model and compare it to other models. It is also used to compare different algorithms and to determine which algorithm is best for a particular task.

Baseline models are often simpler than other models, and they are used to establish a benchmark against which more complex models can be compared. For example, a baseline model might be based on a linear regression or a decision tree. These models are not necessarily the most accurate, but they are a good starting point for more complex models.

Baselines are also used to measure the performance of a model over time. This is done by comparing the performance of a model to the performance of the baseline model. If the performance of the model is better than the baseline, then it means that the model is making progress. If the performance is worse than the baseline, then it means that the model is not making any progress.

Baselines can also be used to compare different algorithms. For example, if two algorithms are being compared for a particular task, then the performance of the baseline model can be used to determine which algorithm is better.

Baselines are also used to evaluate the performance of a model when new data is added to the dataset. This is done by comparing the performance of the model on the new data to the performance of the baseline model on the same data. If the performance of the model is better than the baseline, then it means that the model is making progress. If the performance is worse than the baseline, then it means that the model is not making any progress.

Finally, baselines are also used to compare different models. For example, if two models are being compared for a particular task, then the performance of the baseline model can be used to determine which model is better.

In summary, baseline in Machine Learning is an important concept that is used to measure the performance of a model and compare it to other models. It is also used to compare different algorithms and to determine which algorithm is best for a particular task. Baselines are also used to measure the performance of a model over time, to compare different algorithms, and to evaluate the performance of a model when new data is added to the dataset.