Model fitting involves measuring the accuracy of a machine learning algorithm’s ability to make predictions from data. If a model is well-fitted, it can accurately provide insights and predictions.
The model is fitted by running an algorithm on labeled data, which means that the target variable is already known. The accuracy can be determined by comparing the outcomes provided by the model to the actual observed values of the target variable.
Based on these results, you can continue to adjust parameters and perform feature selection until the model becomes more accurate and reaches an optimal level of performance. In other words, it is improved until it solves the business problem you are facing.
If the model is not well-fitted, it may be overfitted – where the data is matched too closely to the actual results. It can also be under-fitted, where the opposite occurs and the algorithm doesn’t learn well enough the underlying signal in the data
An under-fitted model is inaccurate because it lacks enough data to recognize patterns and make predictions. The size of the dataset does not allow the model to capture underlying trends.
Overfitting refers to the opposite problem – when an algorithm is trained with too much data. This issue causes the model to learn from the noise rather than the actual patterns, so the algorithm can only solve for that specific set of data.
Model fitting is essential to ensure the accuracy of a machine learning model’s outputs. If the algorithm is not well-fitted to the data, the results will not be useful enough to make data-driven decisions.
A well-fitted model gives businesses insights into patterns and underlying trends within their organization and industry.
Remember, accurate predictions are why your organization is creating the machine learning model, to begin with! The worst thing that could happen is that it ends up overfitted or under-fitted and is not able to solve significant problems.
LogicPlum’s machine learning platform will automatically perform the model fitting for you, which will allow your organization to build an accurate machine learning model much quicker. These tools eliminate the need to hire a team of data scientists, saving you time and money.
Our goal is for your organization to gain valuable insights that will help you make strategic, data-driven decisions.