Logistic Regression: Logistic Regression is often considered one of the least computationally expensive algorithms. It is a simple and efficient algorithm, especially when dealing with linearly separable data.

Naive Bayes: Naive Bayes algorithms are computationally inexpensive and can be trained quickly. They are particularly suitable for high-dimensional data, such as text classification.

Decision Trees: Decision Trees are relatively inexpensive to train and understand. However, very deep trees can become computationally more expensive.

Support Vector Machines (SVM): SVMs can be more computationally expensive, especially as the size of the dataset increases. They are powerful for complex decision boundaries but may require more resources.

K-Nearest Neighbors (KNN): KNN can be computationally expensive, especially during the testing phase, as it involves finding the nearest neighbors for each data point.

Neural Networks (Deep Learning): Deep learning algorithms, especially deep neural networks, can be more computationally expensive. Training deep networks requires significant computational power, often relying on specialized hardware like GPUs or TPUs.

Ensemble Methods (Random Forests, Gradient Boosting): Ensemble methods, while powerful, can be more computationally expensive due to the training of multiple models and their combination.

Gradient Boosting Machines: Gradient boosting algorithms like XGBoost or LightGBM can be computationally expensive, especially with large datasets and complex models.

Naive Bayes: Naive Bayes algorithms are often among the fastest for text classification. They have a simple and probabilistic approach that doesn't require extensive computation during training or prediction.

Logistic Regression: Logistic Regression is also known for its speed, especially when dealing with linearly separable data. It is a simple and efficient algorithm.

Decision Trees: Decision Trees can be fast, especially during the prediction phase. However, the speed can depend on the depth and complexity of the tree.

Support Vector Machines (SVM): SVMs can be moderately fast, but their speed may decrease with larger datasets. The use of efficient optimization algorithms and kernel choices can impact the overall speed.

K-Nearest Neighbors (KNN): KNN can be moderate in speed. The prediction phase, in particular, may become slower as the dataset size increases, as it involves finding the nearest neighbors.

Ensemble Methods (Random Forests, Gradient Boosting): While powerful, ensemble methods can have a moderate speed, especially during training, as they involve the construction of multiple models.

Neural Networks (Deep Learning): Deep learning models, including neural networks, can be slower compared to traditional machine learning algorithms, especially during training. Deep learning models often require more computational resources and time to converge.

Gradient Boosting Machines: Gradient boosting algorithms like XGBoost or LightGBM can be slower than simpler models, especially with large datasets and complex models.