Beginner’s Guide to Machine Learning Models
You should learn models such as linear regression, logistic regression, support vector machines, and PCA.
One of the most exciting areas of computer science is machine learning.
Everyone may learn about it, and it has applications in many different industries.
I’m going to go over some of the top machine learning models for beginners in this blog article so you can start using ML!
1. Linear Regression
One of the first machine learning models you should learn about is linear regression. It’s a straightforward method for determining how variables are related, making it simple to grasp.
If you want to calculate house values based on square footage or the number of bedrooms, here is one method! Linear regression generates an equation for the line that best fits the data after training.
Why should you use Linear Regression?
The main advantage of linear regression is that it is relatively easy to understand. After training a model, you can quickly grasp the relationship between two variables, which is useful in some scenarios where you need to explain how your machine learning model makes decisions such as fraud detection or churn prediction.
The linear regression equation is a useful tool for describing the relationship between two variables. It can be used to forecast the values of one variable depending on the values of another.
When should it be used?
- Linear Regression is solely applicable to regression-based situations.
- The dependent and independent variables must have a linear relationship.
- The residuals must be distributed normally.
- There must be no relationship between the features.
- The algorithm assumes that training data is sampled at random.
- Most suited for regression-based applications with linear and simple data relationships.
Advantages
- Excellent interpretability and training speed.
- On linearly separable data, it performs wonderfully.
Disadvantages
- Not robust to outliers.
- This algorithm is also prone to overfitting.
Applications
Linear regression can be used in the following real-world applications:
- Forecasting a home’s price based on square footage or the number of bedrooms
- Predicting sales based on inventory levels and other considerations
- Identifying which elements influence customers’ purchasing decisions
2. Logistic Regression
Another model that you should master early on is logistic regression. When the dependent variable is categorical, logistic regression is used (i.e., has only a limited number of possible values).
It can be used to forecast whether something will happen (for example, whether or not someone will buy a product) or to discover which elements are most significant in producing the desired outcome.
Why use Logistic Regression?
The fundamental advantage of logistic regression is that the results are reasonably simple to interpret. This is because the coefficients (i.e., the estimated values of the parameters) represent how much each variable contributes to the outcome prediction.
Logistic regression works by attempting to find the line that best divides your data into two groups. One group consists of all cases in which the dependent variable is equal to one (i.e., it is predicted that they will purchase the product), while the other group consists of all cases in which the dependent variable is equal to zero (i.e., it is predicted that they will not purchase the product).
When should it be used?
- This approach is only applicable to classification tasks.
- The features and the target variable must have a linear relationship.
- The number of observations must be larger than the number of features.
- Best suited to classification problems where the relationships in the data are both linear and simple.
Advantages
- As with linear regression, this algorithm is highly interpretable and fast to train.
- It performs very well on linearly separable data.
Disadvantages
- Prone to overfitting.
- As with Linear Regression, it does not model complex relationships well.
Applications
You could apply logistic regression in the following real-world contexts:
- Determining the most critical criteria in predicting student success
- Forecasting whether or not a consumer will buy a product according to supply levels and other considerations
- Determining whether or not a person would choose to be a donor based on their preferences and other personal details.
3. K-Nearest Neighbors
KNN is a model that classifies data points based on the points that are most similar to it.
KNN falls in the supervised learning family of algorithms. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure. Classification is done by a majority vote to its neighbors. The data is assigned to the class which has the most nearest neighbors. As you increase the number of nearest neighbors, the value of k, accuracy might increase.
KNN is a non-parametric, lazy learning algorithm. When we say a technique is non-parametric , it means that it does not make any assumptions on the underlying data distribution.
Application
- Predicting customer behavior based on past transactions
- Determining whether a tumor is benign or malignant using medical images
- Segmenting customers into different groups for marketing purposes
Advantages
- No Training Period
- KNN is very easy to implement as the only thing to be calculated is the distance between different points
- As there is no training period thus new data can be added at any time since it wont affect the model.
Disadvantages
- Does not work well with large dataset as calculating distances between each data instance would be very costly.
- Does not work well with high dimensionality as this will complicate the distance calculating process to calculate distance for each dimension.
- Sensitive to noisy and missing data
- Feature Scaling- Data in all the dimension should be scaled (normalized and standardized) properly
4. Naïve Bayes
This algorithm is called “Naive” because it makes a naive assumption that each feature is independent of other features which is not true in real life.
As for the “Bayes” part, it refers to the statistician and philosopher, Thomas Bayes and the theorem named after him, Bayes’ theorem, which is the base for Naïve Bayes Algorithm.
Naïve Bayes algorithm can be defined as a supervised classification algorithm which is based on Bayes theorem with an assumption of independence among features.
The way they work is by using statistical methods to predict whether a new example belongs to one category or another, based on the features it has been assigned.
Application
- Identifying spam emails based on their content
- Predicting the next word that someone will say
- Classifying pictures of animals
Advantages
- It is simple and straightforward to apply.
- It does not needed as much training data.
- It handles both continuous and discrete data
- It scales well in terms of predictors and data points.
- It is fast and can be used to make real-time predictions
- It is not sensitive to irrelevant features
Disadvantages
- Naïve Bayes assumes that all predictors (or features) are independent, rarely happening in real life. This limits the applicability of this algorithm in real-world use cases.
- This algorithm faces the ‘zero-frequency problem’ where it assigns zero probability to a categorical variable whose category in the test data set wasn’t available in the training dataset. It would be best if you used a smoothing technique to overcome this issue.
5. Support Vector Machine
A Support Vector Machine (SVM) is a supervised learning method that can be used for classification as well as regression.
It draws a hyperplane to separate various classes of data in your dataset, increasing the distance between them while attempting to reduce errors caused by mistakenly classifying samples.
Support vector machines have been demonstrated to perform effectively with high-dimensional data, and they are frequently used for text classification and image recognition applications.
Support vector machines have the advantage of being able to generalize well from training data to new examples. This reduces the likelihood of overfitting on the data you train them on, resulting in greater performance in practice.
They also train rather quickly when compared to other machine learning methods.
Application
- Identifying words as nouns or verbs in a sentence
- Classifying an image as a car or truck
- Identifying speech of a person
Advantages
- When there is a clear margin of distinction between classes, SVM works reasonably well.
- SVM performs better in high-dimensional spaces.
- SVM is effective when the number of dimensions exceeds the number of samples.
- SVM uses a small amount of memory.
Disadvantages
- The SVM algorithm is not appropriate for huge data sets.
- When the data set has more noise, i.e. target classes overlap, SVM does not perform well.
- The SVM will underperform when the number of features for each data point exceeds the number of training data samples.
- There is no probabilistic justification for the classification because the support vector classifier works by placing data points above and below the classifying hyperplane.
6. Decision Trees
Another type of machine learning model that is commonly used for classification tasks is decision trees.
They work by dividing the data set into smaller and smaller subsets until each subset contains only instances with similar properties, allowing you to easily classify new examples by examining their features in relation to where they fall in this tree structure.
The main advantage of decision trees is that they are simple to understand and interpret. This is due to the tree structure, which allows you to easily see how each feature contributes to the classification of a new example.
Another advantage of decision trees is that they are relatively resistant to overfitting, which means that even if you have a large amount of data, they will still make good predictions. This is because the tree structure aids in the reduction of noise in data and the separation of different types of examples.
Application
- Identifying plant or animal species
- Estimating house costs
- Customers are classified based on their spending habits.
Advantages:
- Decision trees demand less effort for data preparation during pre-processing than other methods.
- A decision tree does not require normalization of data.
- A decision tree does not require scaling of data as well.
- Missing values in the data have no significant impact on the process of building a decision tree.
- A decision tree paradigm is very natural and simple to communicate to technical teams and stakeholders alike.
Disadvantage
- A minor change in the data can result in a significant change in the structure of the decision tree, resulting in instability.
- For a Decision tree sometimes calculation can go far more complex compared to other algorithms.
- The training period for a decision tree is frequently longer.
- Decision tree training is relatively expensive as the complexity and time has taken are more.
- The Decision Tree algorithm is inadequate for applying regression and predicting continuous values.
7. Random Forest
Random forest is an ensemble machine learning model, which implies it was developed by combining several models into one. Ensemble approaches are well-known for their ability to reduce overfitting.
In the case of random forests, this involves generating several decision trees and then voting them to determine which forecast is better in each instance.
One reason to use a random forest is that it is relatively resistant to overfitting, which means that it will still generate decent predictions even if you have a large amount of data. This is due to the ability of the forest’s individual decision trees to cancel out some of the noise in the input.
Another benefit to using a random forest is that it is simple to comprehend. This is because each decision tree can be viewed at individually and the relationships between them may be shown using a graph called a “forest plot”.
Application
- Predicting the quality of wines
- Classifying galaxies
- determining whether or not a person is likely to develop diabetes in the future
When should it be used?
- This algorithm can be used to solve both classification and regression-based problems.
- It is particularly well suited to large datasets with high dimensionality as the algorithm inherently performs feature selection.
Advantages
- It can model both linear and non-linear relationships.
- It is not sensitive to outliers.
- Random Forest is able to perform well on datasets containing missing data.
Disadvantages
- Random Forest can be prone to overfitting although this can be mitigated to some degree with pruning.
- It is not as interpretable as linear and logistic regression although it is possible to extract feature importance's to give some level of interpretability.
8. K-Means Clustering
K-Means clustering is a cluster analysis technique that is used to group data points into clusters. It can be used to detect patterns in data and enhance the performance of machine learning models.
The accuracy of k-means clustering is determined on the number of clusters examined and how they are defined. However, you can boost the algorithm’s prediction capability by selecting appropriate cluster centers during the algorithm’s initialization stage.
Application
- Partitioning large data sets into a predetermined number of clusters
- Segmenting social networks according to contacts or connections
- Identifying customer segments
Advantages
- Easy to implement
- With a large number of variables, K-Means may be computationally faster than hierarchical clustering (if K is small).
- k-Means may produce Higher clusters than hierarchical clustering
- It can be used on large datasets.
- The resulting clusters are easy to interpret
Disadvantages
- Difficult to predict the number of clusters (K-Value).
- Initial seeds have a strong impact on the final results
- Sensitive to scale.
- K-means are sensitive to outliers
- The results of the clustering are not consistent. If K-means is run on a dataset multiple times it can produce different results each time.
Thanks for taking the time to read my article! If you found it useful, why not hit that follow button on Medium and join my community of like-minded readers? Every clap helps to spread the word and reach even more people, so if you enjoyed the article, please give it a round of applause! By following me, you’ll be the first to know when I publish new content on similar topics. Let’s stay connected and keep learning together!
Are you hungry for more knowledge and eager to explore new ideas? Then you’ll definitely want to check out my other blogs! From fascinating deep dives into cutting-edge technologies to thought-provoking analyses of global trends, there’s something for everyone in my collection. So come on in and discover a world of exciting new topics!
