Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.

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CatBoost for big data: an interdisciplinary review

在Naive Bayes Classifiers模型中，要求父节点下的子节点（特征变量）之间相对独立，然而在现实世界中，特征与特征之间是非独立的、相关的。提出一种预处理方法，实验结果表明，该方法明显地提高了分类精度。

基于Naive Bayes Classifiers的航空影像纹理分类

Data mining is a knowledge discovery process that analyzes data and generate useful pattern from it. Classification is the technique that uses pre-classified examples to classify the required results. Decision tree is used to model classification process. Using feature values of instances, Decision trees classify those instances. Each node in a decision tree represents a feature in an instance to be classified. In this research work ID3, C4.5 and C5.0 Compare with each other. Among all these classifiers C5.0 gives more accurate and efficient result. This research work used C5.0 as the base classifier so proposed system will classify the result set with high accuracy and low memory usage. The classification process generates fewer rules compare to other techniques so the proposed system has low memory usage. Error rate is low so accuracy in result set is high and pruned tree is generated so the system generates fast results as compare with other technique. In this research work proposed system use C5.0 classifier that Performs feature selection and reduced error pruning techniques which are described in this paper. Feature selection technique assumes that the data contains many redundant features. so remove that features which provides no useful information in any context. Select relevant features which are useful in model construction. Crossvalidation method gives more reliable estimate of predictive. Over fitting problem of the decision tree is solved by using reduced error pruning technique. With the proposed system achieve 1 to 3% of accuracy, reduced error rate and decision tree is construed within less time.

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C5.0 Algorithm to Improved Decision Tree with Feature Selection and Reduced Error Pruning

To classify data whether it is in the field of neural networks or maybe it is any application of Biometrics viz: Handwriting classification or Iris detection, feasibly the most candid classifier in the stockpile or machine learning techniques is the Nearest Neighbor Classifier in which classification is achieved by identifying the nearest neighbors to a query example and using those neighbors to determine the class of the query. K-NN classification classifies instances based on their similarity to instances in the training data. This paper presents various output with various distance used in algorithm and may help to know the response of classifier for the desired application it also represents computational issues in identifying nearest neighbors and mechanisms for reducing the dimension of the data.

A Review of Data Classification Using K-Nearest Neighbour Algorithm

Yield prediction is very popular among farmers these days, which particularly contributes to the proper selection of crops for sowing. This makes the problem of predicting the yielding of crops an interesting challenge. Earlier yield prediction was performed by considering the farmer's experience on a particular field and crop. This work presents a system, which uses data mining techniques in order to predict the category of the analyzed soil datasets. The category, thus predicted will indicate the yielding of crops. The problem of predicting the crop yield is formalized as a classification rule, where Naive Bayes and K-Nearest Neighbor methods are used.

Analysis of Soil Behaviour and Prediction of Crop Yield Using Data Mining Approach

This paper focuses on the various techniques that can be implemented for classification of observations that are initially uncategorized. Our objective is to compare the different classification methods and classifiers that can be used for this purpose. In this paper, we study and demonstrate the different accuracies and usefulness of classifiers and the circumstances in which they should be implemented. General Terms Classification, Sentiment, Review, Accuracy, Positive, Negative, Neutral.

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Survey on Classification Techniques for Data Mining

According to several studies, the most influencing factor in a household's energy consumption is user behavior. Changing user behavior to improve energy usage leads to efficient energy consumption, saving money for the consumer and being more friendly for the environment. In this work we propose a framework that aims at assisting households in improving their energy usage by providing real-time recommendations for efficient appliance use. The framework allows for the creation of household-specific and appliance-specific energy consumption profiles by analyzing appliance usage patterns. Based on the household profile and the actual electricity use, real-time recommendations notify users on the appliances that can be switched off in order to reduce consumption. For instance, if a consumer forgets their A/C on at a time that it is usually off (e.g., when there is no one at home), the system will detect this as an outlier and notify the consumer. In the ideal scenario, a household has a smart meter monitoring system installed, that records energy consumption at the appliance level. This is also reflected in the datasets available for evaluating such systems. However, in the general case, the household may only have one main meter reading. In this case, non-intrusive load monitoring (NILM) techniques, which monitor a house's energy consumption using only one meter, and data mining algorithms that disaggregate the consumption into appliance level, can be employed. In this paper, we propose an end-to-end solution to this problem, starting with the energy disaggregation process, and the creation of user profiles that are then fed to the pattern mining and recommendation process, that through an intuitive UI allows users to further refine their energy consumption preferences and set goals. We employ the UK-DALE (UK Domestic Appliance-Level Electricity) dataset for our experimental evaluations and the proof-of-concept implementation. The results show that the proposed framework accurately captures the energy consumption profiles of each household and thus the generated recommendations are matching the actual household energy habits and can help reduce their energy consumption by 2–17%.

Real-time recommendations for energy-efficient appliance usage in households

The present investigation was carried out at the experimental farm, School of Agriculture, Lovely Professional University, Jalandhar, Punjab during 2021-22. The experiment was laid out in a Factorial Randomized Block Design (FRBD) with 3 dates of sowing i.e. D1 (1st October), D2 (15th October) and D3 (30th October) and 4 different levels of fertilizer i.e.75 % RDF, 100 % RDF, 125 % RDF and 150 % RDF altogether 12 treatment combinations of dates of sowing and fertilizer levels replicated thrice. Among different temporal dynamics (sowing dates), D1 (1st October) recorded maximum values for plant height (151.20 cm), primary branches (10.33) and Secondary branches (8.91), Leaf area (64.28 cm2), Fresh weight (242.7 g), Dry weight (28.45 g), CGR (0.558 g m-2day-1), RGR (1.39 g-1 g-1day-1). Regarding the effect of various nutrient levels, the maximum plant height (128.05cm), the number of primary and secondary branches (10.44, 8.44), Leaf area (64.67 cm2), Fresh and dry weight (253.16 g, 29.55 g), CGR (0.581 g m-2day-1), RGR (1.42 g-1 g-1day-1) were recorded in the application of 100% RDF. The interaction of 1st DOS (1st October) × 100% RDF recorded maximum values in terms of most of the growth parameters of commercial importance. So it can be concluded that for better growth and development the toria plants should be sown on 1st October with the application of 100% RDF.

Akshay Jaiswal

Papers

Survey on Classification Techniques for Data Mining

Real-time recommendations for energy-efficient appliance usage in households

Impact of sowing dates and nutrient levels on growth attributes of toria (Brassica campestris var. toria) in trans-gangetic region of Punjab