Search results for Value At Risk

Modelplotr v1.0 now on CRAN: Visualize the Business Value of your Predictive Models
...ic model (let's say the gradient boosted trees model) and on a specific dataset (let's pick the test data). When not specified, modelplotr will set a value for you. The default value for the target class is the smallest target category, in our case term.deposit ; since we want to focus on customers...https://www.kdnuggets.com/2019/06/modelplotrcranbusinessvaluepredictivemodels.html

Evaluating the Business Value of Predictive Models in Python and R
...re we throw more code and output at you, let's get you familiar with the plots we so strongly advocate to use to assess a predictive model's business value. Although each plot sheds light on the business value of your model from a different angle, they all use the same data: Predicted probability...https://www.kdnuggets.com/2018/10/evaluatingbusinessvaluepredictivemodelsmodelplotpy.html

Strategies for Monetizing Big Data
…ond the data and into the economic questions that the data can answer. Often the data can help answer questions about the value, use, risk, or future value or risk of a specific asset. Or the data can say something about an overall market and how asset classes perform and how customers behave…https://www.kdnuggets.com/2015/05/strategiesmonetizingbigdata.html

Would you buy insights from this guy? (How to assess and manage a Data Science vendor)
...logy. Any business outcome or KPI can be used as a target, and if it can be measured, it can be predicted. In a full data diagnostic, the information value of current and potential data sources can be measured against these metrics. Even the value of an “Art of the Possible” POC can be simply and...https://www.kdnuggets.com/2019/11/assessdatasciencevendor.html

What is Your Data Worth? On LinkedIn, Microsoft, and the Value of User Data
...en that active users are less, the value for active users would be higher. Bio: Professor Russell Walker helps companies develop strategies to manage risk and harness value through analytics and Big Data. He is Clinical Associate Professor of Managerial Economics and Decision Sciences at the...https://www.kdnuggets.com/2016/06/walkerlinkedinmicrosoftvalueuserdata.html

Customer Churn Prediction Using Machine Learning: Main Approaches and Models
...value in the products they currently have. Like sales, marketing can engage with customers differently depending on their current indication of churn risk: For example, nonchurn risk customers are better candidates to participate in a case study than a customer who is currently a churn risk,” the...https://www.kdnuggets.com/2019/05/churnpredictionmachinelearning.html

Exclusive Interview: Ajay Bhargava, TCS shares the Big Data Mantra: Harness Data and Harvest Value
...isk evaluation. As an example, evaluating risk by looking at commercial location of a customer, and then superimposing the risk with external weather risk, terrorist risk, geological risks, etc. can greatly enhance the ability of the insurer to not only price the risk more accurately, but also...https://www.kdnuggets.com/2014/09/interviewajaybhargavatcsbigdatamantra.html

Comparing Machine Learning Models: Statistical vs. Practical Significance
...what a pvalue actually is: a pvalue is just a number that measures the evidence against H0: the stronger the evidence against H0, the smaller the pvalue is. If your pvalue is small enough, you have enough credit to reject H0. Luckily, the pvalue can be easily found in R/Python so you don’t...https://www.kdnuggets.com/2019/01/comparingmachinelearningmodelsstatisticalvspracticalsignificance.html

The Big Data Game Board™">The Big Data Game Board™
...s constituents and the data science team to leverage data and analytics to uncover new sources of customer, product, service, channel and operational value (see Figure 4). Figure 4: Data Lake 3.0: The Collaborative Value Creation Platform See the blog “Data Monetization? Cue the Chief Data...https://www.kdnuggets.com/2018/11/bigdatagameboard.html

Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies
...in a coalition with random feature values from the apartment dataset to get a prediction from the machine learning model. When we repeat the Shapley value for all feature values, we get the complete distribution of the prediction (minus the average) among the feature values. SHAP is an enhancement...https://www.kdnuggets.com/2018/12/explainableaimodelinterpretationstrategies.html

Using Linear Regression for Predictive Modeling in R
...if there is a relationship between the dependent and independent variables we are testing. Generally, a large F indicates a stronger relationship. pvalue: This pvalue is associated with the F statistic, and is used to interpret the significance for the whole model fit to our data. Let’s have a...https://www.kdnuggets.com/2018/06/linearregressionpredictivemodelingr.html

Calculating Customer Lifetime Value: SQL Example
...for market survival. Estimating LTV is a predictive metric which depends on future purchases, based on past patterns, and allows you to see how much risk you are exposed to as a business, and how much you can afford to spend to acquire new clients. At an individual level, it also enables you to...https://www.kdnuggets.com/2018/02/calculatingcustomerlifetimevaluesqlexample.html

Interview: Alison Burnham, Scorebig on Optimal, Realtime Pricing through Analytics
...er value seats – seats in prime locations with big discounts but still a high absolute price. AR: Q5. How do you define and measure Customer Lifetime Value? AB: Customer lifetime value is measured for us with an actual return for each customer – for every transaction we know revenue, coupon costs,...https://www.kdnuggets.com/2015/05/interviewalisonburnhamscorebigrealtimepricing.html

Interview: Amit Sheth, Kno.e.sis on Deriving Value from Big Data through Smart Data
...is actionable information— different in each cases. For example, a patient would like to know what he/she can do to avoid an asthma episode. When is risk high enough to take a preventive action (such as using inhaler), and how can this be done while also avoiding overuse? We capture these in terms...https://www.kdnuggets.com/2015/01/interviewamitshethknoesissmartdata.html

Nine Laws of Data Mining, part 2
…member that this new information is not “data”, in the sense of a “given”; it is information only in the statistical sense. 8th Law of Data Mining – “Value Law”: The value of data mining results is not determined by the accuracy or stability of predictive models Accuracy and stability are useful…https://www.kdnuggets.com/2015/06/ninelawsdataminingpart2.html

Naive Bayes: A Baseline Model for Machine Learning Classification Performance
...uracy of the Gaussian model is 0.7440944881889764 The Gaussian model gives us 74% accuracy Advantages of Naive Bayes Can handle missing values Missing values are ignored while preparing the model and ignored when a probability is calculated for a class value. Can handle small sample...https://www.kdnuggets.com/2019/04/naivebayesbaselinemodelmachinelearningclassificationperformance.html

Descriptive Statistics: The Mighty Dwarf of Data Science – Crest Factor
...al number of samples in the data and xi is the ith sample within the data. OK, but what does this really mean? Let us break this down a bit. The peak value is the maximal of the absolute value of all the samples in the data, and the RMS is a kind of a measure of the total “weight” of the data, or...https://www.kdnuggets.com/2018/04/descriptivestatisticsmightydwarfdatasciencecrestfactor.html

Descriptive Statistics: The Mighty Dwarf of Data Science
...e)) 3.0450704320268427 Note: Some implementations of kurtosis use the raw output of the Eq. 1, but often a value of 3 is subtracted from the kurtosis value, so that the value of kurtosis for the Gaussian signal is around 0. Such implementation is often referred to as the “excess kurtosis”. The...https://www.kdnuggets.com/2018/03/descriptivestatisticsmightydwarfdatascience.html

Deep Learning in a Nutshell – what it is, how it works, why care?
...r all of the training examples that we encounter. More formally, if we know that t(i) is the true answer for the ith training example and y(i) is the value computed by the neural network, we want to minimize the value of the error function E: Now at this point you might be thinking, wait up... Why...https://www.kdnuggets.com/2015/01/deeplearningexplanationwhathowwhy.html