Lessons Learned from My First Machine Learning Model

When I started working with ML, I quickly realized there was a gap between theory and what actually happens in practice. This post is about mistakes i made while training my first ml model. I'll walk you through what I learned, what tripped me up, and the lessons that stuck with me. No fluff — just honest notes from someone who went through it.

Introduction to Machine Learning Mistakes

I still remember the excitement of training my first machine learning model. I had spent weeks collecting and preprocessing the data, and finally, it was time to see the results. But, as it often does, reality had other plans. My model's performance was suspiciously high, and it wasn't until later that I realized the mistakes I had made. In this post, I'll share the lessons I learned from those mistakes, in the hopes that you can avoid them in your own machine learning journey.

My Experience with Machine Learning

As a beginner, I made a few critical errors that affected my model's performance. One of the most significant mistakes was training on the full dataset without a proper train/test split. I didn't realize the importance of evaluating my model on unseen data, and as a result, my model was overfitting to the training data. I also didn't check for data leakage, which led to suspiciously high accuracy. Another issue I faced was not scaling my features, which made a huge difference for models like SVM and KNN.

But the biggest mistake I made was using accuracy as the only metric on an imbalanced dataset. My test set contained rows that influenced the scaler fit, which further skewed the results. I never looked at the confusion matrix, which would have given me a better understanding of my model's performance. And, to make matters worse, I tuned hyperparameters on the test set instead of using cross-validation.

Mistakes and Their Consequences

Looking back, I can see that these mistakes were not only avoidable but also crucial to the success of my model. By not splitting my data properly, I was essentially training and testing on the same data, which led to overfitting. Not checking for data leakage meant that my model was memorizing the training data rather than learning from it. And, by not using multiple metrics, I was getting a false sense of my model's performance.

The consequences of these mistakes were severe. My model was not generalizing well to new data, and I was getting poor results on unseen data. It wasn't until I went back and re-trained my model with a proper train/test split, scaled features, and multiple metrics that I started to see decent results.

Data Preprocessing and Splitting

One of the most important lessons I learned was the importance of splitting my data before any preprocessing step. This ensures that my model is not overfitting to the training data and that I'm getting a true sense of its performance. I also learned to use techniques like cross-validation to evaluate my model's performance on unseen data.

Model Evaluation and Metrics

Another crucial lesson I learned was the importance of using multiple metrics to evaluate my model's performance. On an imbalanced dataset, accuracy is not enough, and I needed to use metrics like precision, recall, and F1 score to get a better understanding of my model's performance. I also learned to use the confusion matrix to visualize my model's performance and identify areas for improvement.

Lessons Learned and Best Practices

So, what did I learn from my experience? First and foremost, always split your data before any preprocessing step. This ensures that your model is not overfitting to the training data and that you're getting a true sense of its performance. Second, use multiple metrics to evaluate your model's performance, especially on imbalanced datasets. And third, cross-validation is not optional - it's essential to getting a true sense of your model's performance.

Here's an example of how I implemented these lessons using scikit-learn:

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.svm import SVC

# Load your dataset
# ...

# Split your data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a pipeline with scaling and SVM
pipeline = Pipeline([
    ('scaler', StandardScaler()),
    ('svm', SVC())
])

# Use cross-validation to evaluate your model's performance
scores = cross_val_score(pipeline, X_train, y_train, cv=5)

# Train your model on the training data and evaluate on the test data
pipeline.fit(X_train, y_train)
y_pred = pipeline.predict(X_test)

In this example, I'm using a pipeline to scale my features and train an SVM model. I'm splitting my data into training and testing sets using traintestsplit, and then using crossvalscore to evaluate my model's performance on the training data. Finally, I'm training my model on the training data and evaluating its performance on the test data.

Wrapping Up

Training my first machine learning model was a humbling experience. I made mistakes, but I learned from them, and I'm sharing those lessons with you in the hopes that you can avoid them in your own journey. Remember to always split your data before any preprocessing step, use multiple metrics to evaluate your model's performance, and use cross-validation to get a true sense of your model's performance. With these lessons in mind, you'll be well on your way to building accurate and reliable machine learning models.

Category: Machine Learning

Machine LearningModel TrainingData ScienceBeginnerML MistakesData PreprocessingModel Evaluation

Comments

Post a Comment

Popular posts from this blog

How I Started Learning Data Science as a Beginner (My Roadmap)

Introduction When I decided to learn data science, I had no clear idea where to begin. There were many videos, courses, and opinions online, which made me confused. So I decided to follow a simple learning path instead of learning everything at once. This post shares the roadmap I followed as a beginner. Step 1: Understand What Data Science Is Before coding, I focused on understanding the meaning of data science. Data science mainly involves: • Working with data • Finding patterns • Making decisions using data Understanding this gave me clarity. Step 2: Learn Basic Python Python is the foundation of data science. I started with basic topics like: • Variables • Data types • Conditions • Loops • Functions Daily practice helped me improve slowly. Step 3: Learn Basic Statistics Statistics felt difficult at first, but only basic concepts are required. Important topics include: • Mean, median, and mode • Variance and standard deviation • Probability basics • Correlation Understanding concept...
Read more

Difference Between Artificial Intelligence, Machine Learning, and Data Science

Image
Introduction Artificial Intelligence, Machine Learning, and Data Science are often used interchangeably, but they do not mean the same thing. Many beginners feel confused while starting their learning journey because these terms are closely related. Understanding the difference between AI, ML, and Data Science is very important for students who want to build a career in technology. What is Artificial Intelligence (AI) ?  Artificial Intelligence (AI) refers to the ability of machines to perform tasks that normally require human intelligence. AI systems are designed to: Think logically Make decisions Solve problems Mimic human behavior Examples of Artificial Intelligence: Voice assistants (Alexa, Siri) Chatbots Face recognition systems Self-driving cars AI is the broadest field among the threee What is Machine Learning (ML)? Machine Learning (ML) is a subset of Artificial Intelligence. In Machine Learning, computers learn patterns from data without being expli...
Read more