##### Logloss

Logloss (or Logarithmic Loss) measures classification performance; specifically, uncertainty. This metric evaluates how closely a model’s predicted values are to the actual target value. For example, does a model tend to assign a high predicted value like .90 for the positive class, or does it show a poor ability to identify the positive class and assign a lower predicted value like .40? Logloss ranges between 0 and 1, with 0 meaning that the model correctly assigns a probability of 0% or 100%. Logloss is sensitive to low probabilities that are erroneous.

##### Machine learning

“A field of study that gives computers the ability to learn without being explicitly programmed.” (Arthur Samuel, 1959)

##### Max F1

Max F1 is the point at which predictions will occur or not. When a row’s P1 value is at or above the Max F1, the predicted outcome will happen in the future. If a row’s PO value is below the Max F1, the predicted outcome will not occur. Thus the Max F1 is considered the cutoff point for probabilities to come true in the future or not. This cutoff point is not 50% as you might expect. If you see a prediction that has a probability of occurring (P1) at 90%, Seer may predict that outcome to not occur. This happens because the Max F1 rate is higher than the P1. The key concept to understand about the Max F1 is that Squark uses it to cut the probabilities. So in AI, a prediction may be more than 50% likely to occur and still not be predicted to occur. This AI metric is why you may not see 50/50 cutoff points for predicted probabilities.

##### Mean Absolute Error or MAE

MAE or the Mean Absolute Error is an average of the absolute errors. The smaller the MAE the better the model’s performance. The MAE units are the same units as your data’s dependent variable/target (so if that’s dollars, this is in dollars), which is useful for understanding whether the size of the error is meaningful or not. MAE is not sensitive to outliers. If your data has a lot of outliers, then examine the Root Mean Square Error (RMSE), which is sensitive to outliers.

##### Mean Per Class Error

Mean Per Class Error (in Multi-class Classification only) is the average of the errors of each class in your multi-class data set. This metric speaks toward misclassification of the data across the classes. The lower this metric, the better.

##### Mean Square Error or MSE

MSE is the Mean Square Error and is a model quality metric.  Closer to zero is better.  The MSE metric measures the average of the squares of the errors or deviations. MSE takes the distances from the points to the regression line (these distances are the “errors”) and then squares them to remove any negative signs. MSE incorporates both the variance and the bias of the predictor. MSE gives more weight to larger differences in errors than MAE.

##### Natural Language Processing

The discipline within A.I. that deals with written and spoken language.

##### Pragmatic AI

Pragmatic AI is designed to solve well-defined problems, as opposed to being allowed to seek its own purpose.

##### Predictive Analytics

Statistical techniques gathered from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.

##### Regression

Regression models are the mainstay of predictive analytics. The focus lies on establishing a mathematical equation as a model to represent the interactions between the different variables in consideration. Depending on the situation, there are a wide variety of models that can be applied while performing predictive analytics. Some of them are briefly discussed below.

Linear regression model
The linear regression model analyzes the relationship between the response or dependent variable and a set of independent or predictor variables. This relationship is expressed as an equation that predicts the response variable as a linear function of the parameters. These parameters are adjusted so that a measure of fit is optimized. Much of the effort in model fitting is focused on minimizing the size of the residual, as well as ensuring that it is randomly distributed with respect to the model predictions.

The goal of regression is to select the parameters of the model so as to minimize the sum of the squared residuals. This is referred to as ordinary least squares (OLS) estimation and results in best linear unbiased estimates (BLUE) of the parameters if and only if the Gauss-Markov assumptions are satisfied.

Once the model has been estimated we would be interested to know if the predictor variables belong in the model—i.e. is the estimate of each variable’s contribution reliable? To do this we can check the statistical significance of the model’s coefficients which can be measured using the t-statistic. This amounts to testing whether the coefficient is significantly different from zero. How well the model predicts the dependent variable based on the value of the independent variables can be assessed by using the R² statistic. It measures predictive power of the model i.e. the proportion of the total variation in the dependent variable that is “explained” (accounted for) by variation in the independent variables.

Discrete choice models
Multiple regression (above) is generally used when the response variable is continuous and has an unbounded range. Often the response variable may not be continuous but rather discrete. While mathematically it is feasible to apply multiple regression to discrete ordered dependent variables, some of the assumptions behind the theory of multiple linear regression no longer hold, and there are other techniques such as discrete choice models which are better suited for this type of analysis. If the dependent variable is discrete, some of those superior methods are logistic regression, multinomial logit and probit models. Logistic regression and probit models are used when the dependent variable is binary.

Logistic regression
Main article: Logistic regression
In a classification setting, assigning outcome probabilities to observations can be achieved through the use of a logistic model, which is basically a method which transforms information about the binary dependent variable into an unbounded continuous variable and estimates a regular multivariate model (See Allison’s Logistic Regression for more information on the theory of logistic regression).

The Wald and likelihood-ratio test are used to test the statistical significance of each coefficient b in the model (analogous to the t tests used in OLS regression; see above). A test assessing the goodness-of-fit of a classification model is the “percentage correctly predicted”.

Multinomial logistic regression
An extension of the binary logit model to cases where the dependent variable has more than 2 categories is the multinomial logit model. In such cases collapsing the data into two categories might not make good sense or may lead to loss in the richness of the data. The multinomial logit model is the appropriate technique in these cases, especially when the dependent variable categories are not ordered (for examples colors like red, blue, green). Some authors have extended multinomial regression to include feature selection/importance methods such as random multinomial logit.

Probit regression
Probit models offer an alternative to logistic regression for modeling categorical dependent variables. Even though the outcomes tend to be similar, the underlying distributions are different. Probit models are popular in social sciences like economics.

A good way to understand the key difference between probit and logit models is to assume that the dependent variable is driven by a latent variable z, which is a sum of a linear combination of explanatory variables and a random noise term.