5 Key Terms You Should Know About Machine Learning

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Machine learning as a whole is changing the way that we are assessing various algorithmic approaches for problem-solving in our world. Many developers are using this concept to generate improvements with complex decisions and tasks worldwide. Machine learning does represent the future in algorithmic approaches, and it’s a model that can help us to the advanced technology of a whole. If you’re interested in getting into machine learning, it’s very important that you understand some of the basic concepts involved with the machine learning process and development in machine learning. Here are some of the key terms that you should know about machine learning if you are curious about this technology:

ROC curve:

This term has to do with the varying levels of sensitivity and specificity that is directly represented in the curve with ROC. Understanding which types of levels that should be in use depends on the system and the task itself. ROC curve may be heavily adjusted if there is a need for much higher sensitivity in screening the brain and for clinical trials for medicine for example. This can illuminate false positives within results.

Cross-validation:

Most types of out resume within machine learning work to generate their ongoing performance measures with a term called cross-validation. Training data sets contain a series of subjects in which an algorithm is used to execute predictions by using this model it possible to assign iterations that will improve the overall performance of the model over time.

Mean squared error and mean absolute error:

The relationship between variables can often come up with unreliable results in machine learning. The regression within results can be expressed within the equation using multiple methods for expressing the error. Both of these terms are not directly comparable, but by reducing the mean squared error, it’s possible to come to determinations within machine learning much faster.

Confusion matrix:

Machine learning needs to produce accurate results and using a confusion matrix machine learning bolsters its equations through true positive and false positive rates. Machine learning algorithms also account for accuracy, likelihood ratios, and diagnostic odds ratios. A confusion matrix goes far beyond accuracy and comes down to a ratio of almost every prediction for more direct information from machine learning.

Image segmentation evaluation:

Metric tasks like MSE and ROC are not always useful. Improving the accuracy of image registration means a regular evaluation of databases. Algorithms for machine learning that are introduced to share and examine data can make sure that any competing algorithm will not be drawing from the same data pool. Image segmentation evaluation ensures that the quality of images machine learning programs are using in their study do remain varied.

Consider some of these top terms if you are interested in learning more about machine learning and some of the more interesting terms and solutions that are built into the machine learning process.


Source: https://www.healthimaging.com/topics/artificial-intelligence/5-terms-know-about-machine-learning-algorithms

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