𝐖𝐚𝐲𝐬 𝐓𝐨 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞

The blog post – https://k21academy.com/dp10028 will get an overview of 𝐖𝐚𝐲𝐬 𝐓𝐨 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞

➽ The 𝐠𝐨𝐚𝐥 of a Machine Learning Model is to find out patterns that generalize well on unseen information rather than simply memorizing the information that it had been trained on.

➽ Once your 𝐦𝐨𝐝𝐞𝐥 is prepared, you’d use it to predict the solution on the analysis or take a look at the data set then compare the anticipated target to the particular answer (ground truth)

➽ We are going to discuss the assorted ways 🤯 in which to 𝐞𝐧𝐯𝐢𝐬𝐢𝐨𝐧 𝐭𝐡𝐞 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 of our machine learning model and why to use one in situ of the other

Topics we will cover:

✔️ Confusion matrix

✔️ Accuracy

✔️ Precision

✔️ Recall/Sensitivity/True Positive Rate

✔️ Specificity

✔️ False Positive Rate

✔️ F1 score

✔️ ROC (Receiver Operating Characteristics) curve

✔️ AUC

✔️ RMSE

✔️ R-Squared (R²)

If you are planning to become a certified Azure Data Scientist Associate then register for the FREE Class at https://k21academy.com/dp10002