5 answers
Aravindhan
Student
Erode, Tamil Nadu, India
1
Question
Asked
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how to train and test a machine learning model
As i have the datasets for machine learning, I know to code python, I don't know how to train and test a machine learning model. #tech #machine-learning
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5 answers
Dimitrios Athanasiou
Software Engineer
2
Answers
Thessaloniki, Greece
Updated
Dimitrios’s Answer
Machine learning can be used to provide solutions to a variety of problems. There are many different techniques. Bayesian statistics, gradient descent, boosted trees, neural networks, just to name a few.
As a first step, I would recommend that you take an introduction course on machine learning so that you become familiarized with the various techniques and the problems they can be applied on. There are many courses but I strongly recommend following the Machine Learning course found on the coursera platform and taught by Andrew Ng, a professor at Stanford University. The course is free to take.
I would also suggest looking at Kaggle competitions (https://www.kaggle.com/competitions) and notebooks (https://www.kaggle.com/notebooks). Kaggle notebooks in particular contain dataset analyses other people did and it can provide a great studying material for a new starter.
Machine learning is a fascinating field! I hope you have a great time studying it!
As a first step, I would recommend that you take an introduction course on machine learning so that you become familiarized with the various techniques and the problems they can be applied on. There are many courses but I strongly recommend following the Machine Learning course found on the coursera platform and taught by Andrew Ng, a professor at Stanford University. The course is free to take.
I would also suggest looking at Kaggle competitions (https://www.kaggle.com/competitions) and notebooks (https://www.kaggle.com/notebooks). Kaggle notebooks in particular contain dataset analyses other people did and it can provide a great studying material for a new starter.
Machine learning is a fascinating field! I hope you have a great time studying it!
Nami Verma
Security Consulting
11
Answers
Seattle, Washington
Updated
Nami’s Answer
Hi,
While coding in python, I prefer to use sci-kit learn to divide my dataset into two sets instead of doing this manually.
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
This is the documentation of the function with examples and can help you implement it
All the best!
While coding in python, I prefer to use sci-kit learn to divide my dataset into two sets instead of doing this manually.
https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html
This is the documentation of the function with examples and can help you implement it
All the best!
Aditya Kishore
Engineer
4
Answers
Patna, Bihar, India
Updated
Aditya’s Answer
Hi Aravindhan,
In machine learning you have two types of data as you have mentioned in your question, the training data and the testing data. For the training data, you already have the corresponding answers and you build a model (algorithm) that learns from your training data. Once the model has run on your training data you can use this model to predict results for your test data.
You can follow this link: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
In machine learning you have two types of data as you have mentioned in your question, the training data and the testing data. For the training data, you already have the corresponding answers and you build a model (algorithm) that learns from your training data. Once the model has run on your training data you can use this model to predict results for your test data.
You can follow this link: https://machinelearningmastery.com/machine-learning-in-python-step-by-step/
Rod Hyde
Tech Director
19
Answers
United Kingdom
Updated
Rod’s Answer
Hello, if you haven't come across fast.ai then it is definitely worth some time to find out how to train and test ML models:
https://www.fast.ai/
Try the Introduction to Machine Learning for coders first and then Practical deep learning for coders. You will need some coding experience. If you are not a coder then have a look for free online Python courses.
Hope that helps,
Rod
Ramanandan NK
Software engineer
4
Answers
Bengaluru, Karnataka, India
Updated
Ramanandan’s Answer
Hey Aravindhan!
Let me give you a friendly rundown of the steps to train and test a machine learning model using Python and Scikit-Learn, a popular library for this purpose:
1. **Data Preprocessing**
First, you'll need to get your data ready. This means cleaning it up (taking care of missing values, outliers, and so on), changing it as needed (standardizing, normalizing, etc.), and dividing it into a training set and a test set.
For instance, to split your data with Scikit-Learn, you'd use the `train_test_split` function like this:
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
Here, `X` is your input data, `y` is your output data, `test_size` is the portion of the dataset for testing (0.2 means 20% of data is for testing), and `random_state` sets the seed for random shuffling.
2. **Model Selection**
Pick the right machine learning model for your task. This depends on your problem type (classification, regression, clustering, etc.), your data's size and characteristics, and maybe other factors.
For example, if you're tackling a binary classification problem, you could use a logistic regression model:
```python
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
```
3. **Model Training**
Teach your model using the training data. This is where the actual "learning" takes place.
```python
model.fit(X_train, y_train)
```
4. **Model Evaluation**
Check how well your model does on the training data, usually with a scoring function.
```python
train_score = model.score(X_train, y_train)
print(f'Training score: {train_score}')
```
5. **Model Testing**
Lastly, test your model on the test data. This shows you how it might do on new, unseen data.
```python
test_score = model.score(X_test, y_test)
print(f'Test score: {test_score}')
```
Keep in mind that this is a basic outline, and each step can get more complicated depending on your specific problem. You might need to work with categorical features, address class imbalance, fine-tune hyperparameters, use cross-validation, and so on. But this should give you a great starting point!
Let me give you a friendly rundown of the steps to train and test a machine learning model using Python and Scikit-Learn, a popular library for this purpose:
1. **Data Preprocessing**
First, you'll need to get your data ready. This means cleaning it up (taking care of missing values, outliers, and so on), changing it as needed (standardizing, normalizing, etc.), and dividing it into a training set and a test set.
For instance, to split your data with Scikit-Learn, you'd use the `train_test_split` function like this:
```python
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
Here, `X` is your input data, `y` is your output data, `test_size` is the portion of the dataset for testing (0.2 means 20% of data is for testing), and `random_state` sets the seed for random shuffling.
2. **Model Selection**
Pick the right machine learning model for your task. This depends on your problem type (classification, regression, clustering, etc.), your data's size and characteristics, and maybe other factors.
For example, if you're tackling a binary classification problem, you could use a logistic regression model:
```python
from sklearn.linear_model import LogisticRegression
model = LogisticRegression()
```
3. **Model Training**
Teach your model using the training data. This is where the actual "learning" takes place.
```python
model.fit(X_train, y_train)
```
4. **Model Evaluation**
Check how well your model does on the training data, usually with a scoring function.
```python
train_score = model.score(X_train, y_train)
print(f'Training score: {train_score}')
```
5. **Model Testing**
Lastly, test your model on the test data. This shows you how it might do on new, unseen data.
```python
test_score = model.score(X_test, y_test)
print(f'Test score: {test_score}')
```
Keep in mind that this is a basic outline, and each step can get more complicated depending on your specific problem. You might need to work with categorical features, address class imbalance, fine-tune hyperparameters, use cross-validation, and so on. But this should give you a great starting point!