Let’s say a dataset has n number of features. Now, we may not need all the features to run a machine learning model. If we have more than the necessary features, then that can lead to inefficient inferences or increase the training time of the machine learning model....
What is the F1 score in machine learning? In our previous article, we discussed what precision and recall are in machine learning and how to calculate precision and recall scores using the sklearn Python library. The F1 score is the harmonic mean of precision and...
What are precision and recall in machine learning? In a classification problem, precision and recall are two metrics using which we can measure the performance of the machine learning model. We know that: True Positives (TP): True positives are those output labels...
What is the accuracy score in machine learning? In the case of classification problems, accuracy is a metric using which the performance of the machine learning model can be measured. In this article, we will discuss what accuracy in machine learning is and how we can...
What is the Confusion Matrix? Let’s say we have some features in a dataset. We have the petal length, petal width, sepal length, and sepal width of a flower. And based on these features we need to classify the type of flower. If the output is True, the flower is of...
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