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Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free For collaborations: @love_data
Больше📈 Аналитический обзор Telegram-канала Data Science & Machine Learning
Канал Data Science & Machine Learning (@datasciencefun) языкового сегмента Английский является активным участником. Сейчас сообщество объединяет 75 684 подписчиков, занимая 2 114 место в категории Образование и 4 348 место в регионе Индия.
📊 Показатели аудитории и динамика
С момента создания невідомо проект демонстрирует стремительный рост, собрав аудиторию из 75 684 подписчиков.
Согласно последним данным от 12 июня, 2026, канал показывает стабильную активность. За последние 30 дней изменение числа участников составило 923, а за последние 24 часа — 31, при этом общий охват остаётся высоким.
- Статус верификации: Не верифицирован
- Уровень вовлечённости (ER): Средний показатель вовлечённости аудитории составляет 3.63%. В первые 24 часа после публикации контент обычно набирает 1.36% реакций от общего числа подписчиков.
- Охват публикаций: В среднем каждый пост получает 2 744 просмотров. В течение первых суток публикация набирает 1 026 просмотров.
- Реакции и взаимодействия: Аудитория активно поддерживает контент: среднее количество реакций на один пост — 5.
- Тематические интересы: Контент сосредоточен на ключевых темах, таких как learning, accuracy, distribution, panda, dataset.
📝 Описание и контентная политика
Автор описывает ресурс как площадку для выражения субъективного мнения:
“Join this channel to learn data science, artificial intelligence and machine learning with funny quizzes, interesting projects and amazing resources for free
For collaborations: @love_data”
Благодаря высокой частоте обновлений (последние данные получены 13 июня, 2026) канал поддерживает актуальность и высокий уровень охвата публикаций. Аналитика показывает, что аудитория активно взаимодействует с контентом, что делает его важной точкой влияния в категории Образование.
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✅ Cross Validation & Hyperparameter Tuning 🤖⚙️
👉 Building a model is not enough.
We must also make sure it performs well on unseen data.
This is done using:
✔ Cross Validation
✔ Hyperparameter Tuning
🔹 1. What is Cross Validation?
Cross Validation checks how well a model generalizes to new data.
👉 Instead of using only one train-test split, data is divided multiple times.
🔥 2. K-Fold Cross Validation ⭐
How it Works:
1️⃣ Split data into K parts (folds)
2️⃣ Use one fold for testing
3️⃣ Use remaining folds for training
4️⃣ Repeat until every fold is tested
✅ Example
If K = 5:
• 4 folds → Training
• 1 fold → Testing
Repeated 5 times.
🔹 3. Why Cross Validation is Important?
✔ Better model evaluation
✔ Reduces overfitting risk
✔ More reliable accuracy
🔹 4. Implementation (Python)
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
scores = cross_val_score(model, X, y, cv=5)
print(scores)
from sklearn.model_selection import GridSearchCV
params = {
"n_neighbors": [3,5,7]
}
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✅ Overfitting vs Underfitting 🤖📉
👉 One of the most important concepts in Machine Learning.
A model should not:
❌ Learn too little
❌ Learn too much
It should learn just right ✅
🔹 1. What is Underfitting?
👉 Underfitting happens when the model is too simple and cannot learn patterns properly.
Characteristics:
❌ Poor performance on training data
❌ Poor performance on testing data
✅ Example
Trying to fit a straight line to highly complex data.
🔥 2. What is Overfitting?
👉 Overfitting happens when the model memorizes training data instead of learning general patterns.
Characteristics:
✔ Very high training accuracy
❌ Poor testing accuracy
✅ Example
A student memorizes answers instead of understanding concepts.
🔹 3. Ideal Model (Best Case) ⭐
👉 Performs well on:
✔ Training data
✔ Testing data
This is called: ✅ Good Generalization
🔹 4. Visual Understanding
📉 Underfitting → Too simple
📈 Overfitting → Too complex
✅ Balanced model → Best fit
🔹 5. Causes of Overfitting
✔ Too much model complexity
✔ Small dataset
✔ Too many features
🔹 6. How to Reduce Overfitting ⭐
✔ More training data
✔ Feature selection
✔ Cross-validation
✔ Regularization
✔ Simpler model
🔹 7. How to Reduce Underfitting
✔ Use better features
✔ Increase model complexity
✔ Train longer
🔹 8. Why This is Important?
✔ Critical interview topic
✔ Improves model performance
✔ Core ML concept
🎯 Today’s Goal
✔ Understand overfitting
✔ Understand underfitting
✔ Learn solutions
💬 Tap ❤️ for more!
In a good regression model, the R² score should be:
Which metric is commonly used for classification problems?
✅ Model Evaluation Metrics 📊🤖
👉 After building a Machine Learning model, we must check:
“How good is the model?”
This is done using evaluation metrics.
🔹 1. Why Model Evaluation is Important?
✔ Measures model performance
✔ Detects errors
✔ Helps compare models
✔ Prevents bad predictions
🔥 2. Evaluation Metrics for Regression
Used for predicting numbers
✅ MAE (Mean Absolute Error)
👉 Average absolute error.
MAE = (1/n) Σ |y - ŷ|
✔ Lower MAE = Better model
✅ MSE (Mean Squared Error)
👉 Squares the errors.
MSE = (1/n) Σ (y - ŷ)^2
✔ Punishes large errors more.
✅ RMSE (Root Mean Squared Error)
RMSE = √MSE = √[(1/n) Σ (y - ŷ)^2]
✔ Easy to interpret.
✅ R² Score ⭐
Measures how well model explains data.
R² = 1 - [Σ(y - ŷ)^2 / Σ(y - ȳ)^2]
R² = 1 → Perfect model
✔ Higher R² = Better performance
Where ŷ = predicted value, ȳ = mean of actual values
🔥 3. Evaluation Metrics for Classification
Used for categories
✅ Accuracy
Accuracy = Correct Predictions / Total Predictions
✅ Precision
👉 Out of predicted positives, how many are correct?
Precision = TP / (TP + FP)
✅ Recall
👉 Out of actual positives, how many detected?
Recall = TP / (TP + FN)
✅ F1-Score ⭐
Balance between precision & recall.
F1-Score = 2 (Precision × Recall) / (Precision + Recall)
🔹 4. Confusion Matrix ⭐
A table showing prediction results.
Actual Positive & Predicted Positive = TP (True Positive)
Actual Positive & Predicted Negative = FN (False Negative)
Actual Negative & Predicted Positive = FP (False Positive)
Actual Negative & Predicted Negative = TN (True Negative)
TP = model correctly predicted positive
TN = model correctly predicted negative
FP = model wrongly predicted positive
FN = model wrongly predicted negative
🔹 5. Implementation (Python)
from sklearn.metrics import accuracy_score
y_true = [0, 1, 1, 0]
y_pred = [0, 1, 0, 0]
print(accuracy_score(y_true, y_pred))
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