Зміст курсу
Data Preprocessing
Data Preprocessing
Data Augmentation: Synthetic Data
Data augmentation - is an important step in training machine learning models. This method is understood as an increase in the data sample for training through the modification of existing data. Generating “synthetic” data can be useful in a variety of situations where real-world data may be difficult to obtain, insufficient, or sensitive.
This method is used when there is not enough data to train a machine-learning model. Under the lack of data, we can understand that the dataset may not be representative of the underlying population or phenomenon being studied. The sample size should be large enough to provide sufficient statistical power to detect meaningful relationships or differences. The required sample size depends on factors such as the analysis's complexity, the data's variability, and the desired level of precision. Generating synthetic data can help to supplement real-world data and provide additional training examples.
The pandas library can be used to create synthetic data with a specific structure or format. Here's an example of how to use pandas to create a synthetic dataset:
import pandas as pd import numpy as np # Create a sample dataset dataset = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.choice(['male', 'female'], 10), 'C': np.random.randint(1, 100, 10)}) # Generate synthetic data using Pandas synthetic_data = pd.concat([dataset, dataset.sample(frac=0.5)]) print(synthetic_data)
We use the pd.concat() method to concatenate the original dataframe with a randomly sampled subset of the dataframe. By setting the frac parameter to 0.5, we sample 50% of the rows from the original dataframe and append them to the end of the dataframe, effectively doubling the size of the dataframe and generating synthetic data.
Завдання
Generate a dataset with 4 columns and 5 rows using pandas.
Завдання
Generate a dataset with 4 columns and 5 rows using pandas.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівВсе було зрозуміло?
Data Augmentation: Synthetic Data
Data augmentation - is an important step in training machine learning models. This method is understood as an increase in the data sample for training through the modification of existing data. Generating “synthetic” data can be useful in a variety of situations where real-world data may be difficult to obtain, insufficient, or sensitive.
This method is used when there is not enough data to train a machine-learning model. Under the lack of data, we can understand that the dataset may not be representative of the underlying population or phenomenon being studied. The sample size should be large enough to provide sufficient statistical power to detect meaningful relationships or differences. The required sample size depends on factors such as the analysis's complexity, the data's variability, and the desired level of precision. Generating synthetic data can help to supplement real-world data and provide additional training examples.
The pandas library can be used to create synthetic data with a specific structure or format. Here's an example of how to use pandas to create a synthetic dataset:
import pandas as pd import numpy as np # Create a sample dataset dataset = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.choice(['male', 'female'], 10), 'C': np.random.randint(1, 100, 10)}) # Generate synthetic data using Pandas synthetic_data = pd.concat([dataset, dataset.sample(frac=0.5)]) print(synthetic_data)
We use the pd.concat() method to concatenate the original dataframe with a randomly sampled subset of the dataframe. By setting the frac parameter to 0.5, we sample 50% of the rows from the original dataframe and append them to the end of the dataframe, effectively doubling the size of the dataframe and generating synthetic data.
Завдання
Generate a dataset with 4 columns and 5 rows using pandas.
Завдання
Generate a dataset with 4 columns and 5 rows using pandas.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівВсе було зрозуміло?
Data Augmentation: Synthetic Data
Data augmentation - is an important step in training machine learning models. This method is understood as an increase in the data sample for training through the modification of existing data. Generating “synthetic” data can be useful in a variety of situations where real-world data may be difficult to obtain, insufficient, or sensitive.
This method is used when there is not enough data to train a machine-learning model. Under the lack of data, we can understand that the dataset may not be representative of the underlying population or phenomenon being studied. The sample size should be large enough to provide sufficient statistical power to detect meaningful relationships or differences. The required sample size depends on factors such as the analysis's complexity, the data's variability, and the desired level of precision. Generating synthetic data can help to supplement real-world data and provide additional training examples.
The pandas library can be used to create synthetic data with a specific structure or format. Here's an example of how to use pandas to create a synthetic dataset:
import pandas as pd import numpy as np # Create a sample dataset dataset = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.choice(['male', 'female'], 10), 'C': np.random.randint(1, 100, 10)}) # Generate synthetic data using Pandas synthetic_data = pd.concat([dataset, dataset.sample(frac=0.5)]) print(synthetic_data)
We use the pd.concat() method to concatenate the original dataframe with a randomly sampled subset of the dataframe. By setting the frac parameter to 0.5, we sample 50% of the rows from the original dataframe and append them to the end of the dataframe, effectively doubling the size of the dataframe and generating synthetic data.
Завдання
Generate a dataset with 4 columns and 5 rows using pandas.
Завдання
Generate a dataset with 4 columns and 5 rows using pandas.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівВсе було зрозуміло?
Data augmentation - is an important step in training machine learning models. This method is understood as an increase in the data sample for training through the modification of existing data. Generating “synthetic” data can be useful in a variety of situations where real-world data may be difficult to obtain, insufficient, or sensitive.
This method is used when there is not enough data to train a machine-learning model. Under the lack of data, we can understand that the dataset may not be representative of the underlying population or phenomenon being studied. The sample size should be large enough to provide sufficient statistical power to detect meaningful relationships or differences. The required sample size depends on factors such as the analysis's complexity, the data's variability, and the desired level of precision. Generating synthetic data can help to supplement real-world data and provide additional training examples.
The pandas library can be used to create synthetic data with a specific structure or format. Here's an example of how to use pandas to create a synthetic dataset:
import pandas as pd import numpy as np # Create a sample dataset dataset = pd.DataFrame({'A': np.random.rand(10), 'B': np.random.choice(['male', 'female'], 10), 'C': np.random.randint(1, 100, 10)}) # Generate synthetic data using Pandas synthetic_data = pd.concat([dataset, dataset.sample(frac=0.5)]) print(synthetic_data)
We use the pd.concat() method to concatenate the original dataframe with a randomly sampled subset of the dataframe. By setting the frac parameter to 0.5, we sample 50% of the rows from the original dataframe and append them to the end of the dataframe, effectively doubling the size of the dataframe and generating synthetic data.
Завдання
Generate a dataset with 4 columns and 5 rows using pandas.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів