In the field of machine learning, there is a famous saying: "No free lunch." This phrase emphasizes the need to carefully evaluate and compare different algorithms to find the most suitable one for a specific task. In this recipe, we will spot-check several classification algorithms using cross-validation to assess their performance.
I will skip, or even better, I will leave the steps to be taken before the stop-check algorithms for another recipes.
That includes Summarize Data (Descriptive Statistics, Data Visualization), Prepare data (Data Cleaning, Feature Selection, Data Transform) ...
For our recipe, we will use Sonar Mines vs Rocks dataset. The problem is to predict metal or rock objects from sonar return data. For more information, you can visit the link.
Ingredients:
Step 1. Import the necessary libraries and modules.
Step 2. Read dataset from CSV, convert to NumPy array and split dataset into training and validation sets.
Step 3. Define the models.
Create a dictionary called "models" to store the algorithms with their corresponding names:
I also leave ensemble algorithms for other recipes, like Random Forest, Extra Tree Classifier, Gradient Boosting Classifier, XGBoost. As Albert Einstein said, “Everything should be made as simple as possible, but not simpler”
Step 4. Conduct a spot-check evaluation. Results suggest that LR, KNN, SVM can be candidates for our problem, so we can tune these algorithms in order to get the best results, but before jumping to tuning, we should step back and experiment with other options like different validation size, try to standardize, normalize the dataset and re-run the evaluation, remove unnecessary features, create new features...
The full code can be found on the link.
Spot-checking different classification algorithms is an essential step in machine learning. By comparing the performance of various algorithms using cross-validation, we can gain insights into their strengths and weaknesses. Remember, "No free lunch" reminds us that there is no one-size-fits-all algorithm, and careful evaluation is crucial to find the best approach for a specific problem.
Enjoy experimenting with different algorithms and discovering the most suitable one for your classification task!
Logistic Regression (LR):
Pros:
Cons:
Linear Discriminant Analysis (LDA):
Pros:
Cons:
Quadratic Discriminant Analysis (QDA):
Pros:
Cons:
K-Nearest Neighbors (KNN):
Pros:
Cons:
Decision Tree (CART):
Pros:
Cons:
Naive Bayes (NB):
Pros:
Cons:
Support Vector Machines (SVM):
Pros:
Cons:
Remember that the performance and suitability of each algorithm can vary depending on the specific dataset and problem at hand. It's always recommended to experiment with different algorithms and evaluate their performance using appropriate evaluation metrics and validation techniques.
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