Towards AI

JANUARY 26, 2024

Support Vector Machine Classification and Regression C: This hyperparameter decides the regularization strength. It can have values: [‘l1’, ‘l2’, ‘elasticnet’, ‘None’]. C: This hyperparameter decides the regularization strength. The higher the value of C, the lower the regularization strength.

K-nearest Neighbors 104

K-nearest Neighbors Deep Learning Deep Learning Algorithm 104

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean?

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean?

K-nearest Neighbors 91

K-nearest Neighbors Decision Trees AI AI 91

Towards AI

APRIL 14, 2023

The prediction is then done using a k-nearest neighbor method within the embedding space. Correctly predicting the tags of the questions is a very challenging problem as it involves the prediction of a large number of labels among several hundred thousand possible labels.

K-nearest Neighbors 98

K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

Pickl AI

JULY 31, 2023

Support Vector Machines (SVM) : SVM is a powerful Eager Learning algorithm used for both classification and regression tasks. Popular examples of Eager Learning algorithms include Logistic Regression, Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Pickl AI

AUGUST 28, 2024

Examples include Logistic Regression, Support Vector Machines (SVM), Decision Trees, and Artificial Neural Networks. Instead, they memorise the training data and make predictions by finding the nearest neighbour. Examples include K-Nearest Neighbors (KNN) and Case-based Reasoning.

Machine Learning 52

Machine Learning Machine Learning Algorithm K-nearest Neighbors 52

Mlearning.ai

FEBRUARY 2, 2023

bag of words or TF-IDF vectors) and splitting the data into training and testing sets. Define the classifiers: Choose a set of classifiers that you want to use, such as support vector machine (SVM), k-nearest neighbors (KNN), or decision tree, and initialize their parameters.

K-nearest Neighbors 52

K-nearest Neighbors Support Vector Machines Algorithm Decision Trees 52

IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. Classification algorithms —predict categorical output variables (e.g., “junk” or “not junk”) by labeling pieces of input data.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

Pickl AI

AUGUST 22, 2023

This harmonization is particularly critical in algorithms such as k-Nearest Neighbors and Support Vector Machines, where distances dictate decisions. Scaling steps in as a guardian, harmonizing the scales and ensuring that algorithms treat each feature fairly.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

IBM Journey to AI blog

DECEMBER 19, 2023

This type of machine learning is useful in known outlier detection but is not capable of discovering unknown anomalies or predicting future issues. Isolation forest models can be found on the free machine learning library for Python, scikit-learn.

Machine Learning 82

Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Mlearning.ai

FEBRUARY 17, 2023

Logistic Regression K-Nearest Neighbors (K-NN) Support Vector Machine (SVM) Kernel SVM Naive Bayes Decision Tree Classification Random Forest Classification I will not go too deep about these algorithms in this article, but it’s worth it for you to do it yourself.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

Mlearning.ai

APRIL 6, 2023

For larger datasets, more complex algorithms such as Random Forest, Support Vector Machines (SVM), or Neural Networks may be more suitable. In contrast, for datasets with low dimensionality, simpler algorithms such as Naive Bayes or K-Nearest Neighbors may be sufficient.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Mlearning.ai

JULY 4, 2023

With the preprocessed data in hand, we can now employ pyCaret, a powerful machine learning library, to build our predictive models. pyCaret simplifies the machine learning pipeline by automating various steps, such as feature selection, model training, hyperparameter tuning, and model evaluation.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 52

ODSC - Open Data Science

JANUARY 5, 2024

Scikit-learn is also open-source, which makes it a popular choice for both academic and commercial use.

Python 52

Python K-nearest Neighbors Data Science Data Visualization 52

Towards AI

JULY 19, 2023

among supervised models and k-nearest neighbors, DBSCAN, etc., Feel free to try other algorithms such as Random Forests, Decision Trees, Neural Networks, etc., among unsupervised models.

Machine Learning 40

Machine Learning Machine Learning K-nearest Neighbors Support Vector Machines 40

Pickl AI

SEPTEMBER 3, 2023

Supervised Anomaly Detection: Support Vector Machines (SVM): In a supervised context, SVM is trained to find a hyperplane that best separates normal instances from anomalies. k-Nearest Neighbors (k-NN): In the supervised approach, k-NN assigns labels to instances based on their k-nearest neighbours.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

Pickl AI

JULY 26, 2023

K-Nearest Neighbors with Small k I n the k-nearest neighbours algorithm, choosing a small value of k can lead to high variance. A smaller k implies the model is influenced by a limited number of neighbours, causing predictions to be more sensitive to noise in the training data.

Machine Learning 52

Machine Learning Machine Learning Cross Validation Decision Trees 52

Pickl AI

DECEMBER 9, 2024

Every Machine Learning algorithm, whether a decision tree, support vector machine, or deep neural network, inherently favours certain solutions over others. k-Nearest Neighbors (k-NN) The k-NN algorithm assumes that similar data points are close to each other in feature space.

Machine Learning 52

Machine Learning Machine Learning Decision Trees Natural Language Processing 52

Pickl AI

NOVEMBER 18, 2024

spam detection), you might choose algorithms like Logistic Regression , Decision Trees, or Support Vector Machines. customer segmentation), clustering algorithms like K-means or hierarchical clustering might be appropriate. K-Nearest Neighbors), while others can handle large datasets efficiently (e.g.,

Machine Learning 52

Machine Learning Machine Learning Decision Trees Algorithm 52

Mlearning.ai

FEBRUARY 15, 2023

K-Nearest Neighbou r: The k-Nearest Neighbor algorithm has a simple concept behind it. The method seeks the k nearest neighbours among the training documents to classify a new document and uses the categories of the k nearest neighbours to weight the category candidates [3].

Cross Validation 52

Cross Validation Decision Trees Algorithm Natural Language Processing 52

Pickl AI

SEPTEMBER 12, 2024

K K-Means Clustering: An unsupervised learning algorithm that partitions data into K distinct clusters based on feature similarity. K-Nearest Neighbors (KNN): A simple, non-parametric classification algorithm that assigns a class to a data point based on the majority class of its K nearest neighbours.

Data Analyst 52

Data Analyst Data Science Machine Learning Machine Learning 52

PyImageSearch

SEPTEMBER 16, 2024

For example, in fraud detection, SVM (support vector machine) can classify transactions as fraudulent or non-fraudulent based on historically labeled data. For example, The K-Nearest Neighbors algorithm can identify unusual login attempts based on the distance to typical login patterns.

Clustering 55

Clustering Algorithm Machine Learning Machine Learning 55

Mlearning.ai

MAY 23, 2023

Trade-off Of Bias And Variance: So, as we know that bias and variance, both are errors in machine learning models, it is very essential that any machine learning model has low variance as well as a low bias so that it can achieve good performance. Another example can be the algorithm of a support vector machine.

Data Science 52

Data Science Decision Trees Machine Learning Machine Learning 52

Dataconomy

SEPTEMBER 1, 2023

Hybrid machine learning techniques integrate clinical, genetic, lifestyle, and omics data to provide a comprehensive view of patient health ( Image credit ) The choice of an appropriate model is critical in predictive modeling. Hybrid machine learning techniques excel in model selection by amalgamating the strengths of multiple models.

Decision Trees 195

Decision Trees Machine Learning Machine Learning Support Vector Machines 195

DagsHub

DECEMBER 23, 2024

They are: Based on shallow, simple, and interpretable machine learning models like support vector machines (SVMs), decision trees, or k-nearest neighbors (kNN).   Traditional Active Learning has the following characteristics. Works well with small datasets and models with fewer parameters.

Deep Learning 52

Deep Learning Deep Learning Supervised Learning Clustering 52

AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of treating each input as entirely unique, we can use a distance-based approach like k-nearest neighbors (k-NN) to assign a class based on the most similar examples surrounding the input. To make this work, we need to transform the textual interactions into a format that allows algebraic operations.

Algorithm 75

Algorithm Machine Learning Machine Learning K-nearest Neighbors 75

Dataconomy

DECEMBER 12, 2024

Support Vector Machines (SVM) : A good choice when the boundaries between file formats, i.e. decision surfaces, need to be defined on the basis of byte frequency. K-Nearest Neighbors (KNN) : For small datasets, this can be a simple but effective way to identify file formats based on the similarity of their nearest neighbors.

K-nearest Neighbors 91

K-nearest Neighbors Machine Learning Machine Learning Support Vector Machines 91