When should you not use smote?

You should avoid SMOTE (Synthetic Minority Over-sampling Technique) with noisy or sparse data, high-dimensional categorical features, when classes heavily overlap, for simple problems, or if you need precise probability calibration, as it can introduce noise, create unrealistic samples, or cause data leakage, especially with strong classifiers or complex feature spaces, making simpler methods or class weighting often better.

What are the disadvantages of SMOTE?

By generating synthetic samples based solely on neighboring instances, SMOTE fails to capture more intricate patterns. This can result in the creation of noisy or unrealistic instances, potentially leading to overfitting or diminished generalization performance of the model [35] . ...

When should I use SMOTE?

Use SMOTE (Synthetic Minority Over-sampling Technique) primarily for imbalanced classification problems with numeric/continuous features, especially when the minority class is crucial but underrepresented, to help models learn better from rare events by creating synthetic samples, but apply it after splitting data to prevent leakage and be cautious with high-dimensional or noisy data where other methods (like class weights) might be better.
 

Is SMOTE outdated?

But today, many machine learning engineers treat SMOTE as a relic: occasionally useful, but often harmful or outdated.

Does SMOTE cause overfitting?

Although SMOTE can improve the accuracy of minority class to some extent, it has the risk of introducing noisy instances and overfitting problems because the distribution of adjacent samples is not considered.

How to Solve Multi Class Imbalance Problem using SMOTE in Machine Learning ?? || PYTHON

Which algorithm is best for imbalanced data?

Tree-based algorithms often perform well on imbalanced datasets. Boosting algorithms ( e.g AdaBoost, XGBoost,…) are ideal for imbalanced datasets because higher weight is given to the minority class at each successive iteration. during each interation in training the weights of misclassified classes are adjusted.

Does SMOTE use KNN?

The SMOTE algorithm

The basic principle of SMOTE is to balance the dataset by inserting synthetic samples between minority class samples. It synthesizes new minority class samples through linear interpolation with the k-nearest neighbor algorithm.

Do 87% of data science projects fail?

But only a few people know that most data science projects fail and never make it to production. According to Venture Beat, about 87% of data science projects are never deployed. What is the reason behind this?

What is the 80 20 rule in machine learning?

Be efficient when we develop our machine learning model

The pareto principle or 80/20 rule is a theory that states where that 80% of the effects came from 20% of the causes. In layman's terms, 80% of what happened is caused by 20% of reasons. A smaller number of inputs might have a more significant impact.

Which is better, Adasyn or SMOTE?

SMOTE works well when the imbalance isn't extreme and decision boundaries are relatively clear. ADASYN may perform better when the minority class has complex subclusters or overlaps significantly with the majority class.

Is SMOTE worth it?

Conclusion. SMOTE is a powerful technique for learning from imbalanced data. It helps to balance the class distribution of the original dataset by generating synthetic samples for the minority class. However, it has some limitations, such as no consideration for the quality of synthetic samples and computational cost.

Is it better to oversample or undersample?

In extreme cases where the number of observations in the rare class(es) is really small, oversampling is better, as you will not lose important information on the distribution of the other classes in the dataset.

Is SMOTE deep learning?

We propose Deep synthetic minority oversampling technique (SMOTE), a novel oversampling algorithm for deep learning models that leverages the properties of the successful SMOTE algorithm. It is simple, yet effective in its design.

Should we use SMOTE?

Advantages of SMOTE

This helps avoid overfitting because the new samples are not exact copies of the original samples. Works Well with weak Classifiers: SMOTE can be combined with various machine learning algorithms (such as Random Forest, Logistic Regression, SVM) to improve their performance on imbalanced data.

What are the disadvantages of protocol buffers?

Disadvantages of Protobuf

To improve performance, Protobuf uses a binary format for encoding, which makes the data less readable and will affect the efficiency during the development and testing phase. However, under normal circumstances, Protobuf performs very reliably, and serious problems generally do not occur.

What are the problems with cluster sampling?

Limitations of cluster sampling

Since this method involves studying selected clusters in-depth, the variability within these clusters may not accurately reflect the variability of the entire population. This can lead to biases if the chosen clusters are not representative, potentially skewing the results.

What are the 3 C's of machine learning?

Machine learning is a complex field that requires both technical knowledge and strategic thinking to succeed. One popular framework for understanding the key elements of machine learning is the "3 C's" model, which stands for "Correctness, Consistency, and Completeness."

What is the golden rule of machine learning?

Golden rule of machine learning: – The test data cannot influence training the model in any way.

Is 0.001 a good learning rate?

The learning rate is the most important neural network hyperparameter. It can decide many things when training the network. In most optimizers in Keras, the default learning rate value is 0.001. It is the recommended value for getting started with training.

What are the 5 biggest AI fails?

Here's a list of some of the biggest failures we've seen thus far:

• Chatbots (1999 - Present) For much of the late 1990s and early 2000s, chatbots represented a major breakthrough in the field of AI. ...
• Google's Image Search (2001-2009) ...
• IBM's Watson (2011-present) ...
• Amazon Labels Congress as Criminals (2018) ...
• Tesla (2022)

What is the 30% rule in AI?

The 30% rule in AI refers to guidelines for using artificial intelligence as a partner, not a replacement, suggesting AI should handle about 30% of repetitive tasks, leaving humans to focus on the critical 70% requiring judgment, creativity, and strategy. Alternatively, in education, it's a rule that AI should contribute no more than 30% of the work, with humans providing the other 70% through their own effort and critical thinking, ensuring AI remains a tool, not a crutch. 

Is 40 too late for data science?

Here are 5 lessons I've learned that would've saved me time and self-doubt: 1 You don't need a technical degree 2 Transferable skills are your secret weapon 3 Learning by doing beats passive study 4 Solving business problems gets you noticed 5 It's never too late to pivot ✨Don't let your age or past job define your ...

What is better than KNN?

kNN is precise but computationally intensive, making it less suitable for large datasets. ANN, on the other hand, offers a balance between accuracy and efficiency, making it better suited for large-scale applications.

What are the 4 types of unsupervised learning?

There are several types of unsupervised learning algorithms that are used for clustering, which include exclusive, overlapping, hierarchical, and probabilistic.

When to not use KNN?

Why KNN should not be used for large datasets?

1. The algorithm depends on past observations.
2. Costly to calculate distances on large datasets.
3. Costly to calculate distances on high-dimensional data.
4. Not ideal to store and sort large data.