Search results for Model Risk

Explainable Artificial Intelligence (Part 2) – Model Interpretation Strategies
...nearest neighbors and Naive Bayes! A way to categorize these models based on their major capabilities would be: Linearity: Typically we have a linear model model if the association between features and target is modeled linearly. Monotonicity: A monotonic model ensures that the relationship between...https://www.kdnuggets.com/2018/12/explainableaimodelinterpretationstrategies.html

Human Interpretable Machine Learning (Part 1) — The Need and Importance of Model Interpretation
...nonexperts in machine learning) can understand the choices taken by models in their decisionmaking process (the how, why and what). When comparing models, besides model performance, a model is said to have a better interpretability than another model if its decisions are easier to understand by...https://www.kdnuggets.com/2018/06/humaninterpretablemachinelearningneedimportancemodelinterpretation.html

Modelplotr v1.0 now on CRAN: Visualize the Business Value of your Predictive Models
...Term Deposits.png Even more businesssavvy: Financial plots And there's more! To plot the financial implications of implementing a predictive model, modelplotr provides three additional plots: the Costs & revenues plot, the Profit plot and the ROI plot. So, when you know what the fixed...https://www.kdnuggets.com/2019/06/modelplotrcranbusinessvaluepredictivemodels.html

The Ultimate Guide to Model Retraining
...ictive performance and suggest how frequently models should be retrained. Finally, I’ll mention a few ways to enable model retraining. What is Model Drift? Model Drift refers to a model’s predictive performance degrading over time due to a change in the environment that violates the model’s...https://www.kdnuggets.com/2019/12/ultimateguidemodelretraining.html

Models: From the Lab to the Factory
...fying all of the consumers of the buggy model, they can transition to using the fixed version of the model. In the absence of these steps, we run the risk that model maintenance becomes a challenging process of trying to understand the intentions of the original developer, models deployed to...https://www.kdnuggets.com/2017/04/modelsfromlabfactory.html

Evaluating the Business Value of Predictive Models in Python and R
...terminal and on Windows machines search for cmd.exe and hit enter. Copy and paste the command below and hit enter. pip install git+https://github.com/modelplot/modelplotpy.git Once you have been able to install modelplotpy, it's time to open Python and load it into the working directory and...https://www.kdnuggets.com/2018/10/evaluatingbusinessvaluepredictivemodelsmodelplotpy.html

Using Linear Regression for Predictive Modeling in R
...he data well. Residual standard error: This term represents the average amount that our response variable measurements deviate from the fitted linear model (the model error term). Degrees of freedom (DoF): Discussion of degrees of freedom can become rather technical. For the purposes of this post,...https://www.kdnuggets.com/2018/06/linearregressionpredictivemodelingr.html

Interpretability: Cracking open the black box, Part 2
...und PDPbox to be the most polished. And they also support 2 variable PDP plots as well. from pdpbox import pdp, info_plots pdp_age = pdp.pdp_isolate( model=rf, dataset=X_train, model_features=X_train.columns, feature='age' ) #PDP Plot fig, axes = pdp.pdp_plot(pdp_age, 'Age', plot_lines=False,...https://www.kdnuggets.com/2019/12/interpretabilityblackboxpart2.html

Using Uncertainty to Interpret your Model
...for predicting the likelihood of a user clicking on a content recommendation, also known as CTR (Click Through Rate). Using uncertainty to debug your model The model has many categorical features represented by embedding vectors. The model might have difficulties with learning generalized...https://www.kdnuggets.com/2018/11/usinguncertaintyinterpretmodel.html

Doing Data Science: A Kaggle Walkthrough Part 6 – Creating a Model
...XGBoost algorithm for those skipping ahead), let talk about the approach. Cross Validation As mentioned in regards to decision trees, one of the keys risks when creating models of any type is the risk of overfitting. One of the primary ways data scientists will guard against overfitting is to...https://www.kdnuggets.com/2016/06/doingdatasciencekagglewalkthroughcreatingmodel.html

Four Approaches to Explaining AI and Machine Learning
...his Checklist Explanations of ML models have to meet a variety of requirements to be trustworthy for high stakes business applications such as credit risk modeling, chief among them accuracy, consistency, and performance, i.e., prediction speed. The chart above summarizes how the four techniques...https://www.kdnuggets.com/2018/12/fourapproachesaimachinelearning.html

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead">Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
...RiskSLIM, the RiskSupersparseLinearIntegerModels algorithm (which we’ll also look at in more depth later this week). For both the CORELS and the RiskSLIM models, the key thing to remember is that although they look simple and highly interpretable, they give results with highly competitive...https://www.kdnuggets.com/2019/11/stopexplainingblackboxmodels.html

Introduction to Python Ensembles
...to create a prediction matrix P, where each feature corresponds to the predictions made by a given model, and score each model against the test set: models = get_models() P = train_predict(models) score_models(P, ytest) Model Score svm 0.850 knn 0.779 naive bayes 0.803 mlpnn 0.851 random...https://www.kdnuggets.com/2018/02/introductionpythonensembles.html

Why you need to improve your training data, and how to do it
...you do any other data cleanup, since an intuitive knowledge of what’s in the set will help you make decisions on the rest of the steps. Pick a Model Fast Don’t spend very long choosing a model. If you’re doing image classification, check out AutoML, otherwise look at something...https://www.kdnuggets.com/2018/06/improvetrainingdatahow.html

Many Heads Are Better Than One: The Case For Ensemble Learning
...pecific input variables (like a product segment, income band, or marketing channel) to boost performance even further. Results on a realworld credit risk model To demonstrate the superior performance of ensemble learning, we built a series of binary classification models to predict defaults from a...https://www.kdnuggets.com/2019/09/ensemblelearning.html

How to Monitor Machine Learning Models in RealTime
...r of malfunction in the current model, particularly if we choose the canary with an eye to getting a very stable (even if not so incredibly accurate) model. A canary model is also very handy when we are fielding a potential challenger to our current champion. If we are trying to quantify the risk...https://www.kdnuggets.com/2019/01/monitormachinelearningrealtime.html

How (not) to use Machine Learning for time series forecasting: The sequel
...a feedforward neural network (as illustrated below), instead of a recurrent neural network. I also compare the predictions to that of a random forest model (one of my goto models, based on its simplicity and usually good performance outofthebox). Implementing the models using open source...https://www.kdnuggets.com/2020/03/machinelearningtimeseriesforecastingsequel.html

Solve any Image Classification Problem Quickly and Easily
...‘standing on the shoulder of giants’. In computer vision, transfer learning is usually expressed through the use of pretrained models. A pretrained model is a model that was trained on a large benchmark dataset to solve a problem similar to the one that we want to solve. Accordingly, due to the...https://www.kdnuggets.com/2018/12/solveimageclassificationproblemquicklyeasily.html

How to Rank 10% in Your First Kaggle Competition
...el. Otherwise you wouldn’t be able to reproduce it. Cross Validation Cross validation is an essential step in model training. It tells us whether our model is at high risk of overfitting. In many competitions, public LB scores are not very reliable. Often when we improve the model and get a better...https://www.kdnuggets.com/2016/11/ranktenprecentfirstkagglecompetition.html

Bringing Machine Learning Research to Product Commercialization
...to the original one regarding its data distribution, there is a large drop in accuracy of 4  10% for various previously top performing deep learning models. This demonstrates that the outstanding performance of these models was in many cases based on socalled overfitting  in this case on the...https://www.kdnuggets.com/2018/11/bringingmachinelearningresearchproductcommercialization.html

Introducing Generalized Integrated Gradients (GIG): A Practical Method for Explaining Diverse Ensemble Machine Learning Models
...a unique formula that computes the amount each variable causes the predictive function to change its score. Application to a realworld credit risk model To demonstrate GIG’s capabilities, we used it to explain a mixed ensemble model built from realworld lending data. The model,...https://www.kdnuggets.com/2020/01/generalizedintegratedgradientsexplainingensemblemodels.html

Fighting Overfitting in Deep Learning
...e, you can read more about them here: small batch size, noise in weights. Conclusion Overfitting appears when we have a too complicated model. Our model begins to recognize noisy or random relations, that will never appear again in the new data. One of the characteristics of this...https://www.kdnuggets.com/2019/12/fightingoverfittingdeeplearning.html