- Naive Bayes
- Natural Language Processing (NLP)
- Nearest Neighbor
- Negative Sampling
- Network Compression
- Network Science
- Network Topology
- Network Visualization
- Neural Architecture Search
- Neural Collaborative Filtering
- Neural Differential Equations
- Neural Machine Translation
- Neural Networks
- Neural Style Transfer
- Neural Tangent Kernel
- Neuroevolution
- Neuromorphic Computing
- Node Embedding
- Noise Reduction Techniques
- Non-autoregressive models
- Non-negative Matrix Factorization
- Non-parametric models
- Nonlinear Dimensionality Reduction
- Nonlinear Regression
- Nonparametric Regression
- Normalization
- Novelty Detection
- Numerical Methods
- Numerical Optimization
What is Non-parametric models
Introduction to Non-parametric Models
Non-parametric models are models that do not require certain assumptions regarding the data distribution. These models come in handy when data characteristics are unknown, or when the data does not follow a specific distribution. Non-parametric models have gained popularity, especially in the big data era, where data is massive and complex.Advantages of Non-parametric Models
Non-parametric models have several benefits over parametric models. Some of these benefits are:- Flexibility: Non-parametric models can work on any distribution, regardless of the model's underlying assumption, making them more flexible.
- Robustness: Non-parametric models are robust to outliers.
- Clarity: Non-parametric models are transparent and easy to interpret due to their simplicity.
- Power: Non-parametric models have more power since the number of parameters increases drastically when the sample size is large enough.
Types of Non-parametric Models
There are different non-parametric models that can be used in different scenarios, some of which are as follows:- Kernel Density Estimation: This model estimates the probability density of a random variable by using a kernel function at every data point, which is summed, resulting in a smooth continuous function. It is used to find the density distribution of data.
- Non-parametric Regression: This model is used to fit a response variable to several predictor variables, without a parametric model's assumptions. It is used to discover relationships between variables and build predictive models.
- Permutation Tests: These tests are used to compare two or more groups, where observations of one or more groups are systematically rearranged. They are used to test for differences between populations without assumptions.
- Decision Trees: These models are used to classify data into different categories. These models can handle both categorical and continuous variables.
Applications in Business
Non-parametric models have various applications in the business, some of these applications include:- Customer segmentation: Non-parametric models can be used to divide customers into distinctive segments, which can be used by marketing teams to customize strategies for specific customer segments.
- Forecasting: Non-parametric models can be used in forecasting sales and finance revenue. These models can leverage time-series data to generate accurate forecasts.
- Recommendation systems: Non-parametric models can be used in creating product recommendation systems for e-commerce platforms where product recommendations are personalized to each user.
- Audit Analytics: Non-parametric models can be used in audit analytics, where they can be used to identify fraudulent activities in the system.
Conclusion
Non-parametric models come to save the day when data assumptions cannot be made. Their flexibility, robustness, clarity, and power make them popular in the big data era. These models have different applications in different fields, especially in business. As data continues to grow and become complex, non-parametric models will continue to gain popularity over the traditional parametric model.Loading...