27 Sentences With "target variable" | Random Sentence Generator

"There are several advantages of changing the target variable from the CPI to the CPIF or HICP, and welcomes continued discussion of this question," the central bank said in a statement.

Not all attributes must be used. # The increase in conditional MI of the target variable after building the net equals to the sum of the increase in conditional MI in all layers. # The arcs from terminal nodes to the target variable nodes are weighted (terminal nodes are nodes directly connected to the target variable nodes). The weight is the conditional mutual information due to the arc.

The term trigger (commonly flow or pressure) denotes the criteria that starts inspiration and cycle denotes the criteria that stops it. The target variable should not be confused with the cycle variable or the control variable. The target variable only sets an upper limit for pressure, volume or flow.

The individual point forecasts are used as independent variables and the corresponding observed target variable as the dependent variable in a standard quantile regression setting. The Quantile Regression Averaging method yields an interval forecast of the target variable, but does not use the prediction intervals of the individual methods. One of the reasons for using point forecasts (and not interval forecasts) is their availability. For years, forecasters have focused on obtaining accurate point predictions.

Assuming positive orientation, a scoring rule is considered to be strictly proper if the value of the expected score loss is positive for all possible forecasts. In other words, based on a strictly proper score rule a forecasting scheme must score best if it suggests the target variable as the forecast, and vice versa; i.e. based on a strictly proper score rule a forecasting scheme must score best if, and only if, it suggests the target variable as the forecast.

In predictive analytics and machine learning, the concept drift means that the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. The term concept refers to the quantity to be predicted. More generally, it can also refer to other phenomena of interest besides the target concept, such as an input, but, in the context of concept drift, the term commonly refers to the target variable.

Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.

Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.

Algorithms for constructing decision trees usually work top-down, by choosing a variable at each step that best splits the set of items. Different algorithms use different metrics for measuring "best". These generally measure the homogeneity of the target variable within the subsets. Some examples are given below.

The above analysis of one target variable and one policy tool can readily be extended to multiple targets and tools. In this case a key result is that, unlike in the absence of multiplier uncertainty, it is not superfluous to have more policy tools than targets: with multiplier uncertainty, the more tools are available the lower expected loss can be driven.

For PNN there is one pattern neuron for each category of the target variable. The actual target category of each training case is stored with each hidden neuron; the weighted value coming out of a hidden neuron is fed only to the pattern neuron that corresponds to the hidden neuron’s category. The pattern neurons add the values for the class they represent.

Around 1851, Guerry invented the Ordonnateur Statistique, probably the first mechanical device designed to aid in statistical calculations and the assessment of relationships among moral variables.Larousse, P. (1872) Grand dictionnaire universel du XIX siecle, Paris. Vol. 8, Entry for A-M Guerry This device is now known to have been used for sorting one target variable (e.g., crimes of different types) in relation to other possibly explanatory variables (e.g.

The decision tree can be linearized into decision rules, where the outcome is the contents of the leaf node, and the conditions along the path form a conjunction in the if clause. In general, the rules have the form: : if condition1 and condition2 and condition3 then outcome. Decision rules can be generated by constructing association rules with the target variable on the right. They can also denote temporal or causal relations.

The Generalized Additive Model for Location, Scale and Shape (GAMLSS) is an approach to statistical modelling and learning. GAMLSS is a modern distribution-based approach to (semiparametric) regression. A parametric distribution is assumed for the response (target) variable but the parameters of this distribution can vary according to explanatory variables using linear, nonlinear or smooth functions. In machine learning parlance, GAMLSS is a form of supervised machine learning.

The uniform distribution is useful for sampling from arbitrary distributions. A general method is the inverse transform sampling method, which uses the cumulative distribution function (CDF) of the target random variable. This method is very useful in theoretical work. Since simulations using this method require inverting the CDF of the target variable, alternative methods have been devised for the cases where the cdf is not known in closed form.

The other assignment operator `=` is referred to as a blocking assignment. When `=` assignment is used, for the purposes of logic, the target variable is updated immediately. In the above example, had the statements used the `=` blocking operator instead of `<=`, flop1 and flop2 would not have been swapped. Instead, as in traditional programming, the compiler would understand to simply set flop1 equal to flop2 (and subsequently ignore the redundant logic to set flop2 equal to flop1).

In probability theory and statistics, odds and similar ratios may be more natural or more convenient than probabilities. In some cases the log-odds are used, which is the logit of the probability. Most simply, odds are frequently multiplied or divided, and log converts multiplication to addition and division to subtractions. This is particularly important in the logistic model, in which the log-odds of the target variable are a linear combination of the observed variables.

Input: a list of input variables that can be used, a list of data records (training set) and a minimal statistical significance used to decide whether to split a node or not (default 0.1%). # Create the root node and the layer of the target variable. # Loop until we have used up all the attributes or it cannot improve the conditional mutual information any more with any statistical significance. ## Find the attribute with the maximal conditional mutual information.

In classification problems, an algorithm learns a function to predict a discrete characteristic Y , the target variable, from known characteristics X . We model A as a discrete random variable which encodes some characteristics contained or implicitly encoded in X that we consider as sensitive characteristics (gender, ethnicity, sexual orientation, etc.). We finally denote by R the prediction of the classifier. Now let us define three main criteria to evaluate if a given classifier is fair, that is, if its predictions are not influenced by some of these sensitive variables.

In compiler theory, a reaching definition for a given instruction is an earlier instruction whose target variable can reach (be assigned to) the given one without an intervening assignment. For example, in the following code: d1 : y := 3 d2 : x := y `d1` is a reaching definition for `d2`. In the following, example, however: d1 : y := 3 d2 : y := 4 d3 : x := y `d1` is no longer a reaching definition for `d3`, because `d2` kills its reach: the value defined in `d1` is no longer available and cannot reach `d3`.

Sarizotan (EMD-128,130) is a selective 5-HT1A receptor agonist and D2 receptor antagonist, which has antipsychotic effects, and has also shown efficacy in reducing dyskinesias resulting from long-term anti-Parkinsonian treatment with levodopa. In June 2006, the developer Merck KGaA announced that the development of sarizotan was discontinued, after two sarizotan Phase III studies (PADDY I, PADDY II) failed to meet the primary efficacy endpoint and neither the Phase II findings nor the results from preclinical studies could be confirmed. No statistically significant difference of the primary target variable between sarizotan and placebo could be demonstrated.

Filter feature selection is a specific case of a more general paradigm called Structure Learning. Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph. The most common structure learning algorithms assume the data is generated by a Bayesian Network, and so the structure is a directed graphical model. The optimal solution to the filter feature selection problem is the Markov blanket of the target node, and in a Bayesian Network, there is a unique Markov Blanket for each node.

From a theoretical perspective, the institutions of labour market impact the behaviour of labour supply and labour demand, and further impacts the decisions of wage setting and hiring. By establishing comparable data, factors such as labour tax system, employment protection, or unemployment benefit system are centralized to empirical macro-economic researches (Zeilstra & Elhorst, 2014). These researches look for cross- national differential sources in the performance of labour market. Majority of the researchers identified the target variable of unemployment as it was most suitable for reflecting the ability of economy to avoid the possible consequence of involuntary joblessness.

SuperPascal enforces certain restrictions on the use of variables and communication to minimise or eliminate time-dependent errors. With variables, a simple rule is required: parallel processes can only update disjoint sets of variables. For example, in a `parallel` statement a target variable cannot be updated by more than a single process, but an expression variable (which can't be updated) may be used by multiple processes. In some circumstances, when a variable such as an array is the target of multiple parallel processes, and the programmer knows its element-wise usage is disjoint, then the disjointness restriction may be overridden with a preceding `[sic]` statement.

The American Psychological Association recommends a holistic approach to reducing recidivism rates among offenders by providing "cognitive–behavioral treatment focused on criminal cognition" or "services that target variable risk factors for high-risk offenders" due to the numerous intersecting risk factors experienced by mentally ill and non-mentally ill offenders alike. To prevent the recidivism of individuals with mental illness, a variety of programs are in place that are based on criminal justice or mental health intervention models. Programs modeled after criminal justice strategies include diversion programs, mental health courts, specialty mental health probation or parole, and jail aftercare/prison re-entry. Programs modeled after mental health interventions include forensic assertive community treatment and forensic intensive case management.

The above discussion assumed a static world in which policy actions and outcomes for only one moment in time were considered. However, the analysis generalizes to a context of multiple time periods in which both policy actions take place and target variable outcomes matter, and in which time lags in the effects of policy actions exist. In this dynamic stochastic control context with multiplier uncertainty, a key result is that the "certainty equivalence principle" does not apply: while in the absence of multiplier uncertainty (that is, with only additive uncertainty) the optimal policy with a quadratic loss function coincides with what would be decided if the uncertainty were ignored, this no longer holds in the presence of multiplier uncertainty.

In macroeconomics, multiplier uncertainty is lack of perfect knowledge of the multiplier effect of a particular policy action, such as a monetary or fiscal policy change, upon the intended target of the policy. For example, a fiscal policy maker may have a prediction as to the value of the fiscal multiplier—the ratio of the effect of a government spending change on GDP to the size of the government spending change—but is not likely to know the exact value of this ratio. Similar uncertainty may surround the magnitude of effect of a change in the monetary base or its growth rate upon some target variable, which could be the money supply, the exchange rate, the inflation rate, or GDP. There are several policy implications of multiplier uncertainty: (1) If the multiplier uncertainty is uncorrelated with additive uncertainty, its presence causes greater cautiousness to be optimal (the policy tools should be used to a lesser extent).