At least, we can explain the model mistakes in a way humans can understand, instead of talking about numeric scores.With all the recent advances in Machine Learning, the better our models are, the more complex they become.

However, each community addresses the problem from a di erent per-spective and provides a di erent meaning to explanation. For example, when working with texts, it is common to vectorize words into a numeric N dimension vector, using This article was meant to show the reasons why explaining and interpreting Machine Learning models are so important, and how some of these tools work. They are simpler models that can be used to explain a more complex model. Explaining machine learning models to the business General summaries. But it also makes them inherently hard to explain, especially to non-data scientists. DSC Webinar Series: Critical Behaviors of Data-Driven Companies To not miss this type of content in the future, However, if it’s too high, it may overlook classes with only a few samples.Because of the naive assumption (hence the name) that variables are independent given the class, we can rewrite P(X|y) as follows:4 Pandas Tricks that Most People Don’t Know10 Cool Python Project Ideas for Python DevelopersLong Short-Term Memory Networks Are Dying: What’s Replacing It?Logistic Regression is a classification technique that also finds a ‘line of best fit’.
The only difference is that the penalty is calculated with the absolute value of the slope instead.AdaBoost is a boosted algorithm that is similar to Random Forests but has a couple of significant differences:Also, since we are solving for y, P(X) is a constant which means that we can remove it from the equation and introduce a proportionality.Support Vector Machines are a classification technique that finds an optimal boundary, called the hyperplane, which is used to separate different classes. Trends in data modeling. In explainable ML we make predictions using a complicated black box model (e.g., a DNN), and use a second (posthoc) model created to explain what the first model is doing. This encompasses many tasks, including automatically classifying documents, using machines to translate between languages, or … Photo by Alex Bertha on Unsplash. A residual is equal to the actual minus predicted value.K-Nearest Neighbours is a classification technique where a new sample is classified by looking at the nearest classified points, hence ‘K-nearest’. For this kind of explanation, we can use Even for those who work daily with Machine Learning, it is hard to explain how the models we create work and how they take a decision. Data collection is not always a cheap task, so, knowing the most relevant features, we can better use our resources to obtain quality data in the most efficient way.T-distributed Stochastic Neighbor EmbeddingUnifying Word Embeddings and Matrix Factorization — Part 1Unfortunately, our eyes can only see at most 3 dimensions.
Specific summaries. Teachable Machine Train a computer to recognize your own images, sounds, & poses. Modern machine learning models are usually applied in a black-box manner: only the input (data) and output (predictions) are considered, the inner workings are considered too complex to understand. Explain Why? The system supports:

Linear Regression. Although their results are very nice, it is extremely important to explain these models' methods for those who are paying for it.Now, on this case, we can see that the word "model" was the biggest responsible for the mistake, the word "puzzle" tried to point to the right label, but it wasn't enough. Many different explanations can be generated which

However, a medic application that defines a person needs a surgery based on its exams must count with a very transparent explanation about its decision.

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