Kaggle Introducing ML

Feature Columns and Label Columns

In machine learning tasks, data is typically organized in a tabular format, where each row represents a sample and each column represents a feature or label. Feature columns are used to describe the attributes of a sample, while label columns are used to describe the target value of a sample.

Feature Columns

Feature columns can be continuous, discrete, categorical, or mixed. For example, in a house price prediction task, feature columns might include the house's area, number of bedrooms, number of bathrooms, and location.

The selection of feature columns is an important step in a machine learning task. Choosing the right feature columns can help the model learn the relationships between samples better, thus improving the performance of the model.

There are many methods for feature selection, common methods include:

• Manual selection: Choose feature columns based on expert experience or intuition.
• Statistical methods: Evaluate the importance of feature columns using statistical methods, and then select important feature columns.
• Machine learning methods: Use machine learning models to automatically select feature columns.

Label Columns

Label columns are used to describe the target value of a sample. The type of label columns is usually continuous or discrete.

In a machine learning task, the goal of the model is to predict the value of the label column based on the feature columns. For example, in a house price prediction task, the label column is the price of the house.

The selection of label columns is relatively simple, usually based on the goal of the task.

The Use of Feature Columns and Label Columns

In machine learning tasks, feature columns and label columns are typically used for the following two purposes:

• Training the model: Feature columns and label columns are used to train the model. The model learns the relationship between feature columns and label columns, thus establishing the model.
• Predicting the target value: A trained model can be used to predict the target value of new samples.

For example, in a house price prediction task, feature columns and label columns can be used to train a linear regression model. The linear regression model learns the linear relationship between feature columns and label columns, thus establishing the model. A trained model can be used to predict the price of new houses.

Conclusion

Feature columns and label columns are an important part of machine learning tasks. Choosing the right feature columns and label columns can help the model learn the relationships between samples better, thus improving the performance of the model.

Additional Tips

In addition to the tips mentioned in the above response, here are some additional tips for working with feature columns and label columns in machine learning:

• Consider the data types of feature columns. Different data types require different types of processing. For example, continuous data can be processed using statistical methods, while categorical data can be processed using machine learning methods.
• Clean and prepare the data. Before training a model, it is important to clean and prepare the data. This includes removing outliers, filling in missing values, and transforming the data to a suitable format.
• Use a variety of evaluation metrics. It is important to use a variety of evaluation metrics to evaluate the performance of a model. This will help you to understand the strengths and weaknesses of the model.

Define, Fit, Predict, and Evaluate

Define:

• This stage involves choosing the appropriate model architecture for your problem. Decision trees are suitable for certain tasks, while other problems might benefit from linear regression, neural networks, or even more complex models.
• You also need to specify parameters like the number of features, the depth of the decision tree, or the number of layers in a neural network. These parameters influence the model's capacity and complexity.

Fit:

• This is indeed the heart of modeling. During this stage, the model "learns" from the provided data. It analyzes the patterns and relationships between features and the target variable, adjusting its internal parameters to capture these relationships effectively.

Predict:

• Once trained, the model can be used to make predictions on new data. For example, given a new data point with unknown features, a trained decision tree would navigate its branches based on the values of those features, ultimately reaching a leaf node and providing a predicted value for the target variable.

Evaluate:

• Evaluating the model's accuracy is crucial to assess its effectiveness. This involves comparing the model's predictions on a separate set of data (validation or test data) with the actual values of the target variable. Common metrics for evaluation include accuracy, precision, recall, and F1 score.

Remember, choosing the right model, tuning its parameters, and evaluating its performance are iterative processes. You might need to adjust your approach and try different models or settings to achieve optimal results.

Coding

import pandas as pd
    
# Load data
melbourne_file_path = '/kaggle/input/melb-data/melb_data.csv'
melbourne_data = pd.read_csv(melbourne_file_path) 
# Filter rows with missing values
melbourne_data = melbourne_data.dropna(axis=0)
# Choose target and features
y = melbourne_data.Price
melbourne_features = ['Rooms', 'Bathroom', 'Landsize', 'BuildingArea', 
                        'YearBuilt', 'Lattitude', 'Longtitude']
X = melbourne_data[melbourne_features]

from sklearn.model_selection import train_test_split

# split data into training and validation data, for both features and target
# The split is based on a random number generator. Supplying a numeric value to
# the random_state argument guarantees we get the same split every time we
# run this script.
train_X, val_X, train_y, val_y = train_test_split(X, y,random_state = 0)

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error

forest_model = RandomForestRegressor(random_state=1)
forest_model.fit(train_X, train_y)
melb_preds = forest_model.predict(val_X)
print(mean_absolute_error(val_y, melb_preds))