{"id":4211,"date":"2020-08-03T04:31:40","date_gmt":"2020-08-03T04:31:40","guid":{"rendered":"https:\/\/dataaspirant.com\/?p=4211"},"modified":"2023-04-28T16:33:05","modified_gmt":"2023-04-28T11:03:05","slug":"confusion-matrix-sklearn-python","status":"publish","type":"post","link":"https:\/\/dataaspirant.com\/confusion-matrix-sklearn-python\/","title":{"rendered":"Best Confusion Matrix Guide With Sklearn Python"},"content":{"rendered":"
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\"Confusion

Today is last for you to confuse about confusion matrix. <\/p>\n

Before that, how many times your read about confusion matrix, and after a while forgot about the ture positive, false negative<\/strong> … etc, Even you implemented confusion matrix with sklearn or tensorflow, Still we get confusion about the each componets of the matrix.<\/p>\n

As I said before, after reading this article, you will never forget confusion matrix any more.<\/p>\n

So forgot everything you learned so far, and start fresh now. <\/p>\n

The machine learning model<\/strong><\/a> building journey end goal is not about building the model. The real journey will begin when we start measuring the performance of the model we built. <\/p>\n

We are having numerous ways to quantify the performance of the model<\/strong><\/a>. The confusion matrix<\/strong> is one such case, This model performance metrics is popularly used for quantifying the performance of the classification models<\/a><\/strong> which fall under the supervised learning algorithms<\/strong><\/a>.<\/p>\n

Before we drive further let me explain what you are about to learn in this article.<\/p>\n

Why we need a confusion matrix<\/h2>\n

Let’s understand what is the need of using the confusion matrix as performance metrics for the classification models.<\/p>\n

Accuracy is the popular model evaluation method used for the majority of the classification models in supervised learning algorithms<\/a>.<\/strong> This makes us to think about the below question.  <\/p>\n

When we are having accuracy as a measure for knowing the performance of the classification models then why we need another measure to quantify the performance of the model?<\/p>\n

Let\u2019s understand this with an example.<\/p>\n

 Suppose if we are building a binary classification model<\/a> for imbalanced target class data.<\/p>\n

Let me give you an example of an imbalanced dataset. In the target class imbalance dataset, the target classes are not properly balanced.<\/p>\n

What is target class imbalance<\/h3>\n

Let’s say we are having two expected<\/strong> classes for the target variable.<\/p>\n

Which are<\/p>\n

    \n
  1. Positive <\/li>\n
  2. Negative<\/li>\n<\/ol>\n

    \"Class

    95%<\/strong> percentage we are getting a positive class and only 5%<\/strong> percentage we’re getting the negative class. Here positive class is dominating<\/strong> the negative class, this kind of in balance of the target class within the target classes is called imbalance<\/strong>.<\/p>\n

    Examples of the imbalanced dataset<\/h3>\n

     Below are some of the examples with the imbalance dataset.<\/p>\n