Logistic regression model
In classification problems your output variable can take on only one of a few possible values instead of any number. The logistic regression model is a popular and widely used learning algorithm, an example of this type of classification problem is an email spam filter, the answer you output is going to be either 0 (not spam) or 1 (spam). Classification problems where there are only two possible outputs are called binary classifications.
"Logistic regression is a statistical analysis method to predict a binary outcome, such as yes or no, based on prior observations of a data set." Tech target
k-Nearest Neighbors (kNN)
The k-Nearest Neighbors (kNN) algorithm is a simple and intuitive machine learning algorithm used for classification and regression tasks. It is a type of instance-based or lazy learning algorithm, meaning it doesn't build an explicit model during the training phase. Instead, it stores the entire training dataset and makes predictions based on the similarity between the new data point and the existing data points.
Basics of kNN
Input Data: Utilizes a labeled training dataset with examples, each featuring a set of attributes and corresponding class labels or target values.
Distance Metric: Depends on a chosen distance metric (e.g., Euclidean or Manhattan distance) to measure similarity between data points in the feature space.
Prediction Process: When classifying or predicting a new data point, the algorithm calculates distances to all points in the training dataset.
k Nearest Neighbors: Selects the k nearest neighbors (smallest distances) to the new data point, with 'k' being a user-defined parameter.
Majority Vote (Classification) or Average (Regression):
Classification: Assigns the most common class label among the k neighbors through a majority vote.
Regression: Computes the average (or weighted average) of the target values of the k nearest neighbors."
Steps in kNN Algorithm
Choose the value of k: Specify the value of 'k, which represents the number of neighbors considered when making predictions. The choice of k can significantly impact the model's performance.
Calculate distances: Use the chosen distance metric to compute the distance between the new data point and all data points in the training set.
Identify neighbors: Select the 'k' data points with the smallest distances as the nearest neighbors.
Make prediction: For classification tasks, assign the most common class label among the 'k' neighbors. For regression, predict the average (or weighted average) of the target values based on the neighbors.
Challenges and Considerations with k-Nearest Neighbors (kNN)
Sensitivity to Feature Scaling: kNN is sensitive to the scale of features, so it's often essential to normalize or standardize the features before applying the algorithm.
Computational Cost: Since kNN compares the new data point to all points in the training set, it can be computationally expensive, especially with large datasets.
Choice of Distance Metric: The performance of kNN can be influenced by the choice of distance metric, and different metrics may be more suitable for different types of data.
Use Cases
Classification: kNN is often used in classification problems, such as identifying the category of a new document, classifying images, or predicting the type of disease based on patient data.
Regression: It can also be applied to regression tasks, like predicting house prices based on similar properties in the neighborhood.
Anomaly Detection: kNN can be used for anomaly detection by identifying data points that are significantly dissimilar to their neighbors.
In summary, the kNN algorithm proves versatile and straightforward for prediction tasks, relying on the similarity of data points. Its effectiveness and simplicity make it a popular choice, particularly for small to moderately-sized datasets.