Whenever I read articles about data science I feel like there is some important aspect missing: evaluating the performance and quality of a machine learning model. There is always a neat problem at hand that gets solved and the process of data acquisition, handling and model creation is discussed, but the evaluation aspect too often is very brief. But I truly believe it's the most important fact, when building a new model. Consequently, the first post on this blog will deal with a pretty useful evaluation technique: lift analysis. Machine learning covers a wide variety of problems like regression and clustering.

Whenever I read articles about data science I feel like there is some important aspect missing: evaluating the performance and quality of a machine learning model. Consequently, the first post on this blog will deal with a pretty useful evaluation technique: lift analysis. When evaluating machine learning models there is a plethora of possible metrics to assess performance.

Whenever I read articles about data science I feel like there is some important aspect missing: evaluating the performance and quality of a machine learning model. There is always a neat problem at hand that gets solved and the process of data acquisition, handling and model creation is discussed, but the evaluation aspect too often is very brief. But I truly believe it's the most important fact, when building a new model. Consequently, the first post on this blog will deal with a pretty useful evaluation technique: lift analysis. Machine learning covers a wide variety of problems like regression and clustering.