KNN vs Decision Tree in Machine Learning

There are numerous machine learning algorithms available, each with its strengths and weaknesses depending on the scenario. Factors such as the size of the training data, the need for accuracy or interpretability, training time, linearity assumptions, the number of features, and whether the problem is supervised or unsupervised all influence the choice of algorithm. It’s essential to choose an algorithm carefully based on these factors. In this article, we will compare two popular algorithms, Decision Trees and K-nearest Neighbor (KNN), discussing their workings, advantages, and disadvantages in various scenarios.

What are Decision Trees?

Decision trees are a type of machine-learning algorithm that can be used for both classification and regression tasks. They operate by picking up basic judgment rules derived from the characteristics of the data. The target variable’s value may then be predicted for fresh data samples using these criteria.

The internal nodes of decision trees represent features, the branches represent decision rules, and the leaf nodes represent predictions. Decision trees are represented as tree structures. Recursively dividing the data into progressively smaller groups according to the feature values is how the algorithm operates. The algorithm selects the characteristic at each node that divides the data into groups with distinct goal values.

Advantages of Decision Tree Algorithms

• Simple to comprehend and interpret: People with no prior experience with machine learning may grasp and interpret decision trees with ease. They are therefore a wise option for situations where the ability to explain the model’s predictions is crucial.
• Versatile: Classification, regression, and anomaly detection are just a few of the machine learning applications that decision tree algorithms may be used to.
• Robust to noise: Decision tree algorithms are relatively robust against data noise. This is because their projections are based on the overall pattern of the data rather than specific data points.

Limitations and Considerations

• Overfitting: Decision trees have the potential to overfit and so capture data noise. This problem can be reduced by using methods like pruning, restricting the depth of the tree, or establishing minimum samples per leaf.
• Bias: Features with higher levels may be favored by some tree topologies. This bias can be addressed by properly scaling features or by utilizing gain ratio-considering algorithms like as C4.5.

What is KNN?

KNN is one of the most basic yet essential classification algorithms in machine learning. It is heavily used in pattern recognition, data mining, and intrusion detection and is a member of the supervised learning domain.

Since it is non-parametric, which means it does not make any underlying assumptions about the distribution of data (unlike other algorithms like GMM, which assume a Gaussian distribution of the provided data), it is extensively applicable in real-life circumstances. An attribute-based previous data set (also known as training data) is provided to us, allowing us to classify locations into groups.

Advantages of the KNN Algorithm:

1. Easy Implementation: It is a straightforward algorithm to implement, making it a good choice for beginners.
2. Adaptability: The algorithm adapts easily to new examples or data points. Since it stores all the data in memory, when new data is added, it adjusts itself and incorporates the new information into future predictions.
3. Few Hyperparameters: KNN has few hyperparameters, namely the value of k (number of neighbors) and the choice of distance metric. This simplicity in parameter tuning makes it easy to use and experiment with different configurations.

Disadvantages of the KNN Algorithm:

1. Scalability Issue: Due to its “lazy” nature, KNN stores all the training data and compares it to every new data point during prediction. This makes it computationally expensive and time-consuming, especially for large datasets. It requires significant data storage for the entire training set, which becomes impractical with massive datasets.
2. Curse of Dimensionality: As the number of features (dimensions) in your data increases, the effectiveness of KNN drops. This phenomenon is known as the “curse of dimensionality.” In high-dimensional space, finding truly similar neighbors becomes difficult, leading to inaccurate classifications.
3. Overfitting: Due to the challenges with high dimensionality, KNN is susceptible to overfitting, where the model memorizes the training data too closely and fails to generalize well to unseen data .To mitigate this, techniques like feature selection and dimensionality reduction are often used, adding complexity to the process.

KNN vs Decision Tree

Property	KNN	Decision Tree
Training	Does not require specific training.	Require a proper training phase.
Learning	Zero learning, that is why called lazy algorithm.	Builds a model based on training.
Data Availability	Data must always be available for making predictions.	Once training is completed training data is not needed.
Interpretability	Most interpretable algorithm.	It is also interpretable since we can see decision making in tree format.
Prediction Time	Takes much time in decision making, since it have to traverse the whole dataset to calculate the distance with each datapoint.	Once training is completed, prediction time is less.
Training Time	Since no training is required hence it zero(0).	Require initial training time to create decision nodes and branches.
Scalability	Since it needs to store the training data also, hence memory intensive, and cannot be used for large datasets.	Once the training is complete and tree is built, it can be used for large datasets.
Overfitting	Since distance is measured, hence becomes sensitive to noise.	Pruning can stop overfitting, if not pruned property is susceptible to overfitting.
External Parameters.	K value (Number of neighbours to be considered).	Pruning parameters, and depth of the tree are hyper parameters.
Usage	Suitable for small size dataset, sometime used for medium sized dataset, but for large sized dataset it becomes computationally expensive.	Used for small as well as large size dataset.