The machine learning algorithm is often fed with massive amounts of data. Hence, machine learning accuracy dramatically depends on the correctness of this data. If the data is not accurate, the benefits of machine learning will quickly fall apart. Here we have discussed why model accuracy matters in your ML algorithms.
What Does Machine Learning Model Accuracy Mean?
It is a measurement used to ascertain which algorithm model best recognizes patterns and relationships between variables in a data set. The data set could be based on training data or system input. The model should be able to generalize “unobserved” data and produce better insights and predictions. This way, model accuracy plays a critical role in delivering more business value.
Why Does It Matter?
Businesses depend on machine learning models to make the right decisions. The more accurate model will give you better outcomes/insights that will lead to fast-moving bottom lines.
The cost of erroneous insights can be huge. Hence, companies must optimize machine learning models for greater accuracy, which will reduce the risks and mitigate the costs in practical/better business decisions. Improving machine learning accuracy can save you a considerable amount of money, time, and unwanted stress.
Different Types of Learning in ML Models
To understand accuracy in the machine learning model, you need to understand the different kinds of learning involved in ML.
In this method, the dependent variable is mentioned, and you need to predict according to it. It can be categorized into two types.
- Regression – the dependent variable is a continuous feature. For example, you have to predict house data prices continuously.
- Classification – the dependent variable is expressed in binary. For example, you have to predict whether a person will survive or not in an accident.
Unsupervised Learning– In this learning method, there is no mention of the dependent variable. This technique is useful for e-commerce companies to use data and create prediction patterns to target the most valued customers.
How Is Accuracy Calculated?
The method of calculating accuracy depends on the type of learning used in the model.
Mean Square Error – In the mean square error formula, you need three inputs like a number of data points, observed values, and predicted values. It would help if you derived the average of the observed and predicted values. Mean Square error is a risk function. It considers the expected value of square error loss.
It is an essential element in the ML model as it helps determine the classification model’s performance. It is specifically used to calculate accuracy, specificity, sensitivity, and precision. The confusion matrix generally focuses on True Positive, False Negative, True Negative, and False Positive.
- In True Positive, both observation and prediction are positive.
- In False Negative, the observation is positive, but it is predicted as negative.
- In True Negative, both the observation and prediction are negative.
- In False Positive, the observation is negative, but the prediction is positive.
To sum up, today’s business decisions are centered on big data and artificial intelligence. Like humans, we want model assessments to be right in all aspects. The accuracy of the model is essential in ML as business decisions are based on these models to improve output further.