Optimization | Machine Learning | Data Science
How to Choose the Best Algorithm for Your Machine Learning Project
Optimizing ML Model Performance: A Guide to Algorithm Selection
“Came for data , stayed for science” - Kirk Borne ,Chief Science Officer at DataPrime, Inc
Choosing the right classification & Regression machine learning algorithm is critical to building an accurate predictive model. However, with a wide range of algorithms available, it can be challenging to decide which one to use for a particular dataset.
⚠ You can solve the below-mentioned questions from this blog ⚠
✔ What if I am building Low code — No code ML automation tool and I do not have any orchestrator or memory management system ? will my data help in this ?
✔ What if my CPU memory is insufficient and makes things time-consuming in the case of random search in multiple hyperparameter tuning at a time?
✔ how to reduce the complexity and computational expensiveness of ML models ?
✔ Data tells everything, you just need to take a call!!
In this article, we will discuss some of the factors to consider while selecting a classification & Regression machine learning algorithm based on the characteristics of the data.
We know that understanding data plays a crucial role in the selection of algorithms, but in a general sense, people use all algorithms and on the basis of accuracy or other performance matrices, they further decide to fine-tune.
If you can understand the data then you even do not need to use all algorithms. other than fitting all, you can directly use case-specific algorithms to perform your tasks. Let's understand this in detail.
Thumb rules I have understood by my experience:
Let’s talk about Classification
▶ Size of the Dataset: The size of the dataset is an essential factor to consider when selecting a classification algorithm. For small datasets, algorithms that are less complex and have fewer parameters, such as Naive Bayes, may be a good choice. For larger datasets, more complex algorithms such as Random Forest, Support Vector Machines (SVM), or Neural Networks may be more suitable.
▶ Type of Data: The type of data you have can also affect the choice of the classification algorithm. For example, if you have binary or categorical data, you may want to consider using algorithms such as Logistic Regression, Decision Trees, or Random Forests. For continuous data, algorithms such as Linear Regression or SVM may be more appropriate.
▶ The dimensionality of the Data: The number of features or attributes in your dataset, also known as dimensionality, can influence the choice of the classification algorithm. For datasets with high dimensionality, algorithms such as SVM or Random Forests that can handle a large number of features may be a better choice. In contrast, for datasets with low dimensionality, simpler algorithms such as Naive Bayes or K-Nearest Neighbors may be sufficient.
▶ Distribution of the Data: The distribution of the data can also affect the choice of algorithm. For example, if the data is normally distributed, algorithms such as Logistic Regression or Linear Discriminant Analysis may work well. For non-normal or skewed data, algorithms such as Decision Trees or SVM may be more appropriate.
▶ Number of Classes: The number of classes or categories in your dataset is an essential consideration when selecting a classification algorithm. For datasets with only two classes, algorithms such as Logistic Regression or Support Vector Machines can be used. For datasets with more than two classes, algorithms such as Decision Trees, Random Forests, or Neural Networks can be used.
▶ Imbalanced Classes: If your dataset has imbalanced classes, where the number of instances in one class is much larger or smaller than the others, you may need to use specialized algorithms that can handle such situations. For example, for datasets with imbalanced classes, algorithms such as Random Forests, Boosted Trees, or SVMs with different kernel functions can be used.
▶ Speed and Resource Constraints: The time and computational resources required to train and run a model can also affect the choice of algorithm. Some algorithms, such as Decision Trees or Naive Bayes, are fast and require fewer resources. In contrast, algorithms such as Neural Networks or SVMs may be slower and require more computational power and memory.
⚠ Bonus tip ⚠
Always start with KNN!!!!!!!
Surprised?
Let me tell you the reasons.
[1] KNN is a lazy learner and computationally inexpensive than tree-based algorithms.
[2] In many use cases, data points are overlapped because of outliers and their complex nature. In this case, boundary-based algorithms struggle and either they overfit or can't make partitions.
[3] KNN doesn't work on the boundary and it directly finds distances on basis of closeness so even though data points are overlapped, KNN works nicely.
Let's talk about regression
▶ Linear regression: Use linear regression when the relationship between the independent and dependent variables is linear. This algorithm works best when the number of independent variables is small.
▶ Polynomial regression: Use polynomial regression when the relationship between the independent and dependent variables is curvilinear. This algorithm can capture non-linear relationships but can lead to overfitting if the degree of the polynomial is too high.
▶ Ridge regression: Use ridge regression when the data has a multicollinearity problem, which means the independent variables are highly correlated with each other.
▶ Lasso regression: Use lasso regression when you have a large number of independent variables, and you want to select the most important ones.
▶ Elastic net regression: Use elastic net regression when you have a large number of independent variables, and some of them are highly correlated.
▶ Decision tree regression: Use decision tree regression when the relationship between the independent and dependent variables is not linear or when there are interactions between the independent variables.
▶ Random forest regression: Use random forest regression when the dataset is large, and there are many independent variables.
▶ Support vector regression: Use support vector regression when the relationship between the independent and dependent variables is non-linear and when there is a need to capture outliers.
In conclusion, selecting the right classification & Regression machine learning algorithm for a particular dataset is a crucial step in building an accurate predictive model. To make the best decision, you should consider factors such as the size, type, dimensionality, distribution, number of classes, and imbalanced classes of the data, as well as any speed or resource constraints. By taking into account these factors, you can choose an algorithm that is well-suited to your data and optimize the performance of your model.
I wrote a similar article related to choosing the right CNN architecture for your project. If you are interested then you can read it.
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