Machine learning model accuracy is one way to evaluate classification models. Essentially it is a metric that, based on the training data, uncovers which model best identifies relationships and patterns between variables within a dataset.
In other words, accuracy is the percentage of predictions each model got right – the number of accurate predictions divided by the total number of predictions.
Although model evaluation does not consider accuracy alone, it is the prime metric when comparing data models.
Due to the significant number of mathematical techniques involved in modeling and the uncertain nature of data, it is also essential to consider how the model will perform on a different dataset and how much flexibility it offers.
Models that are accurate and effective at generalizing unseen data are better at forecasting future events and therefore provide more value to your business.
You look to machine learning models to help make practical business decisions. When you use models that produce more accurate outcomes, you get better information and can, therefore, make better decisions.
The cost of wrong information can be high, so mitigating that risk by optimizing model accuracy is essential.
Naturally, identifying the point where returns begin to diminish is also crucial so that you don’t spend excess time and money developing models that won’t move the needle very much.
LogicPlum’s automated machine learning platform leverages the best open-source algorithms to enable its users – even those with no programming background – to build, deploy, and maintain machine learning models for your organization. By incorporating data science and data analytics best practices, users produce accurate and efficient models with high levels of interpretability. This makes it easier to explain the resulting insights to regulatory agencies as well as stakeholders.
LogicPlum exhaustively tests the accuracy of its models with cross-validation that eliminates sampling bias. Also, LogicPlum’s automated machine learning platform provides extensive insights that reduce the occurrence of problems like data leakage that can negatively impact the model’s results and render decision making less effective.
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