An alternative to current mainstream preprocessing methods is proposed: Value Selection (VS). Unlike the existing methods such as feature selection that removes features and instance selection that eliminates instances, value selection eliminates the values (with respect to each feature) in the dataset with two purposes: reducing the model size and preserving its accuracy. Two probabilistic methods based on information theory's metric are proposed: PVS and P + VS. Extensive experiments on the benchmark datasets with various sizes are elaborated. Those results are compared with the existing preprocessing methods such as feature selection, feature transformation, and instance selection methods. Experiment results show that value selection can achieve the balance between accuracy and model size reduction.

Decision trees are simple to implement and equally easy to interpret. And decision trees are idea for machine learning newcomers as well! If you are unsure about even one of these questions, you've come to the right place! Decision Tree is a powerful machine learning algorithm that also serves as the building block for other widely used and complicated machine learning algorithms like Random Forest, XGBoost, and LightGBM. You can imagine why it's important to learn about this topic!

We compare machine learning explainability methods based on the theory of atomic (Shapley, 1953) and infinitesimal (Aumann and Shapley, 1974) games, in a theoretical and experimental investigation into how the model and choice of integration path can influence the resulting feature attributions. To gain insight into differences in attributions resulting from interventional Shapley values (Sundararajan and Najmi, 2019; Janzing et al., 2019; Chen et al., 2019) and Generalized Integrated Gradients (GIG) (Merrill et al., 2019) we note interventional Shapley is equivalent to a multi-path integration along $n!$ paths where $n$ is the number of model input features. Applying Stoke's theorem we show that the path symmetry of these two methods results in the same attributions when the model is composed of a sum of separable functions of individual features and a sum of two-feature products. We then perform a series of experiments with varying degrees of data missingness to demonstrate how interventional Shapley's multi-path approach can yield less consistent attributions than the single straight-line path of Aumann-Shapley. We argue this is because the multiple paths employed by interventional Shaply extend away from the training data manifold and are therefore more likely to pass through regions where the model has little support. In the absence of a more meaningful path choice, we therefore advocate the straight-line path since it will almost always pass closer to the data manifold. Among straight-line path attribution algorithms, GIG is uniquely robust since it will still yield Shapley values for atomic games modeled by decision trees.

Let's start with a thought experiment that will illustrate the difference between a decision tree and a random forest model. Suppose a bank has to approve a small loan amount for a customer and the bank needs to make a decision quickly. The bank checks the person's credit history and their financial condition and finds that they haven't re-paid the older loan yet. Hence, the bank rejects the application. But here's the catch – the loan amount was very small for the bank's immense coffers and they could have easily approved it in a very low-risk move. Therefore, the bank lost the chance of making some money.

Data Science is one of the most dynamic fields in technology attracting innumerable candidates towards it. However, not everyone ends up landing on a good Data Scientist profile. With the cut-throat competition among the candidates, you need to have the edge to have an upper hand. Therefore, it is very much important for the aspirants to know those common and tricky questions that are asked by the interviews. Before going through the Interview question, it is suggested that you get you acquire the fundamental knowledge of Data Science.

Machine learning has proved invaluable for a range of different tasks, yet it also proved vulnerable to evasion attacks, i.e., maliciously crafted perturbations of input data designed to force mispredictions. In this paper we propose a novel technique to verify the security of decision tree models against evasion attacks with respect to an expressive threat model, where the attacker can be represented by an arbitrary imperative program. Our approach exploits the interpretability property of decision trees to transform them into imperative programs, which are amenable for traditional program analysis techniques. By leveraging the abstract interpretation framework, we are able to soundly verify the security guarantees of decision tree models trained over publicly available datasets. Our experiments show that our technique is both precise and efficient, yielding only a minimal number of false positives and scaling up to cases which are intractable for a competitor approach.

Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.

Quality is determined by Accuracy and completeness. Companies use machine learning models to make practical business decisions, and more accurate model outcomes result in better decisions. The cost of errors can be huge, but optimizing model accuracy mitigates that cost. Machine Learning model accuracy is a measurement used to determine which model is best at identifying relationships and patterns between variables in a dataset based on the input, or training data. The better a model can generalize to'unseen' data, the better predictions and insights it can produce, which in turn deliver more business value. The dataset which I have chosen is the Breast Cancer Prediction dataset.

The cost of acquiring new customers is high, so companies are spending more on customer loyalty and retention. Identifying the total value generated by a customer in the entire customer life cycle would help companies in business campaigns and in other activities. So naturally Customer Relationship Management (CRM) becomes a key element of modern marketing strategies. If we can predict a score that allows us to project, on a given population, quantifiable information then it can be used by the information system (IS) to personalize the customer relationship. KDD (Knowledge Discovery and Data Mining) Cup 2009 challenge consists of three tasks, predicting the churn, appentency and upselling, through the data provided by the telecom company Orange.

A decision tree is a simple tree-like structure constituting nodes and branches. At each node, data is split based on any of the input features, generating two or more branches as output. This iterative process increases the numbers of generated branches and partitions the original data. This continues until a node is generated where all or almost all of the data belong to the same class and further splits -- or branched -- are no longer possible. This whole process generates a tree-like structure.