KNN Algorithm is used to find the class of point by the class of nearest neighbour.

KNN Algorithm can be used for both classification as well as Regression! but here we will be using to solve Classification problem.

Here, in the dataset, We are having 4 features which are Gender, Age, Salary, Purchase Iphone.

The table KNN data is part of the dataset Extended GHCNd Station Coverage (KNN algorithm), available at https://redivis.com/datasets/kqpk-a1jj1pen4. It contains 2867 rows across 4 variables.

Dataset

This dataset was created by Gökalp Olukcu

Released under Data files © Original Authors

Contents

K-Nearest Neighbors (KNN) is a popular supervised learning algorithm used for classification and regression tasks. It is a non-parametric algorithm that makes predictions based on the similarity between input features and their neighboring data points.

In the context of a KNN dataset, it typically refers to a dataset that is suitable for applying the KNN algorithm. Here are a few characteristics of a dataset that can work well with KNN:

Numerical features: KNN works with numerical features, so the dataset should contain numerical attributes. If categorical features are present, they need to be converted into numerical representations through techniques like one-hot encoding or label encoding.

Similarity measure: KNN relies on a distance metric to determine the similarity between data points. Common distance measures include Euclidean distance, Manhattan distance, and cosine similarity. The dataset should have features that can be effectively compared using a distance metric.

Feature scaling: Since KNN uses distance calculations, it's generally a good practice to scale the features. Features with larger scales can dominate the distance calculations and lead to biased results. Common scaling techniques include standardization (subtracting mean and dividing by standard deviation) or normalization (scaling values to a range, e.g., 0 to 1).

Sufficient data points: KNN performs best when the dataset has a sufficient number of data points for each class or target value. Having too few instances per class can lead to overfitting or inaccurate predictions.

It's important to note that the suitability of a dataset for KNN depends on the specific problem and domain. It's always recommended to analyze and preprocess the dataset based on its characteristics before applying any machine learning algorithm, including KNN.

Abstract

This dataset contains extended weather station coverage data by conducting a naive KNN algorithm.

Actual classification performance for Promoter dataset using KNN classifier.

Actual classification performance for WDBC dataset using KNN classifier.

Dataset

This dataset was created by george saavedra

Contents

Description of datasets used for evaluation and comparison.

Dataset Overview

This dataset provides k-nearest neighbor (kNN) target distributions for language modeling. Each token in the Wikipedia corpus is associated with a soft probability distribution over its top-k nearest neighbors in the representation space of a frozen language model. These targets can be used to train MLP Memory.

Corresponding Preprocessed Corpus: Rubin-Wei/enwiki-dec2021-preprocessed-mistral Compatible Model: Mistral-7B-v0.3 Paper: MLP Memory: A Retriever-Pretrained… See the full description on the dataset page: https://huggingface.co/datasets/Rubin-Wei/kNN-Targets-wikipedia-mistral.

Actual classification performance for miRNA dataset using KNN classifier.

Selecting a model in predictive toxicology often involves a trade-off between prediction performance and explainability: should we sacrifice the model performance to gain explainability or vice versa. Here we present a comprehensive study to assess algorithm and feature influences on model performance in chemical toxicity research. We conducted over 5000 models for a Tox21 bioassay data set of 65 assays and ∼7600 compounds. Seven molecular representations as features and 12 modeling approaches varying in complexity and explainability were employed to systematically investigate the impact of various factors on model performance and explainability. We demonstrated that end points dictated a model’s performance, regardless of the chosen modeling approach including deep learning and chemical features. Overall, more complex models such as (LS-)SVM and Random Forest performed marginally better than simpler models such as linear regression and KNN in the presented Tox21 data analysis. Since a simpler model with acceptable performance often also is easy to interpret for the Tox21 data set, it clearly was the preferred choice due to its better explainability. Given that each data set had its own error structure both for dependent and independent variables, we strongly recommend that it is important to conduct a systematic study with a broad range of model complexity and feature explainability to identify model balancing its predictivity and explainability.

I conducted my analysis using the Titanic dataset from Kaggle's 'Titanic: Machine Learning from Disaster' competition. This dataset includes information about passengers on the RMS Titanic, such as their demographics, ticket class, and whether they survived or not. The dataset is commonly used for predictive modeling tasks, and my goal was to apply machine learning techniques to predict passenger survival based on various features.

Quantifying spatially explicit or pixel-level aboveground forest biomass (AFB) across large regions is critical for measuring forest carbon sequestration capacity, assessing forest carbon balance, and revealing changes in the structure and function of forest ecosystems. When AFB is measured at the species level using widely available remote sensing data, regional changes in forest composition can readily be monitored. In this study, wall-to-wall maps of species-level AFB were generated for forests in Northeast China by integrating forest inventory data with Moderate Resolution Imaging Spectroradiometer (MODIS) images and environmental variables through applying the optimal k-nearest neighbor (kNN) imputation model. By comparing the prediction accuracy of 630 kNN models, we found that the models with random forest (RF) as the distance metric showed the highest accuracy. Compared to the use of single-month MODIS data for September, there was no appreciable improvement for the estimation accuracy of species-level AFB by using multi-month MODIS data. When k > 7, the accuracy improvement of the RF-based kNN models using the single MODIS predictors for September was essentially negligible. Therefore, the kNN model using the RF distance metric, single-month (September) MODIS predictors and k = 7 was the optimal model to impute the species-level AFB for entire Northeast China. Our imputation results showed that average AFB of all species over Northeast China was 101.98 Mg/ha around 2000. Among 17 widespread species, larch was most dominant, with the largest AFB (20.88 Mg/ha), followed by white birch (13.84 Mg/ha). Amur corktree and willow had low AFB (0.91 and 0.96 Mg/ha, respectively). Environmental variables (e.g., climate and topography) had strong relationships with species-level AFB. By integrating forest inventory data and remote sensing data with complete spatial coverage using the optimal kNN model, we successfully mapped the AFB distribution of the 17 tree species over Northeast China. We also evaluated the accuracy of AFB at different spatial scales. The AFB estimation accuracy significantly improved from stand level up to the ecotype level, indicating that the AFB maps generated from this study are more suitable to apply to forest ecosystem models (e.g., LINKAGES) which require species-level attributes at the ecotype scale.

Dataset

This dataset was created by Abu Noman Md. Sakib

Contents

Few-shot learning classification accuracy on 14 medical datasets with the KNN classifier.

This dataset contains information about car evaluations based on various features like buying price, maintenance cost, number of doors, capacity of persons, luggage boot size, and safety rating. It also includes the class label indicating the overall evaluation of the car.

The dataset is ideal for machine learning classification tasks, especially for understanding the impact of categorical and numerical features on car evaluations.

Key Points:

Total Rows: 1,728 (Example, replace with actual row count) Total Columns: 7 Categorical Features: buying, maint, lug_boot, safety, class Numerical Features: doors, persons Objective: Predict the class of a car based on the given features.

Text classification, as an important research area of text mining, can quickly and effectively extract valuable information to address the challenges of organizing and managing large-scale text data in the era of big data. Currently, the related research on text classification tends to focus on the application in fields such as information filtering, information retrieval, public opinion monitoring, and library and information, with few studies applying text classification methods to the field of tourist attractions. In light of this, a corpus of tourist attraction description texts is constructed using web crawler technology in this paper. We propose a novel text representation method that combines Word2Vec word embeddings with TF-IDF-CRF-POS weighting, optimizing traditional TF-IDF by incorporating total relative term frequency, category discriminability, and part-of-speech information. Subsequently, the proposed algorithm respectively combines seven commonly used classifiers (DT, SVM, LR, NB, MLP, RF, and KNN), known for their good performance, to achieve multi-class text classification for six subcategories of national A-level tourist attractions. The effectiveness and superiority of this algorithm are validated by comparing the overall performance, specific category performance, and model stability against several commonly used text representation methods. The results demonstrate that the newly proposed algorithm achieves higher accuracy and F1-measure on this type of professional dataset, and even outperforms the high-performance BERT classification model currently favored by the industry. Acc, marco-F1, and mirco-F1 values are respectively 2.29%, 5.55%, and 2.90% higher. Moreover, the algorithm can identify rare categories in the imbalanced dataset and exhibit better stability across datasets of different sizes. Overall, the algorithm presented in this paper exhibits superior classification performance and robustness. In addition, the conclusions obtained by the predicted value and the true value are consistent, indicating that this algorithm is practical. The professional domain text dataset used in this paper poses higher challenges due to its complexity (uneven text length, relatively imbalanced categories), and a high degree of similarity between categories. However, this proposed algorithm can efficiently implement the classification of multiple subcategories of this type of text set, which is a beneficial exploration of the application research of complex Chinese text datasets in specific fields, and provides a useful reference for the vector expression and classification of text datasets with similar content.

Average standard errors from 1,000 experiments are shown inside the parentheses; boldface values indicate in each dataset the KNN models with minimum error rates.

P-KNN Precomputed Scores Dataset

This dataset provides precomputed pathogenicity prediction scores generated by the P-KNN method using dbNSFP v5.2 (academic or commercial version) with joint calibration.It contains pathogenicity assessments for all missense variants, organized into multiple subfolders.

  Dataset Structure





  1. precomputed_score_academic_chromosome

Includes precomputed scores derived from the academic version of dbNSFP v5.2a, organized by genomic… See the full description on the dataset page: https://huggingface.co/datasets/brandeslab/P-KNN.