A. Splitting the data into groups based on some criteria
B. Concatenating multiple data frames
C. Performing element-wise operations on data frames
D. Sorting data in ascending or descending order
A. A clustering algorithm that groups similar data points
B. A technique that reduces overfitting by using a combination of decision trees trained on different subsets of the data
C. A process that reduces the dimensionality of the dataset
D. A method used to select the best hyperparameters for a model
A. Evolution through mutation and crossover
B. Random initialization of weights
C. Fitness evaluation based on a predefined objective
D. Manual selection of hyperparameters
A. Keras
B. TensorFlow
C. Matplotlib
D. Pandas
A. A large standard deviation means that data points are more spread out
B. A small standard deviation means that data points are close to the mean
C. Standard deviation can only be used for categorical data
D. Standard deviation is not influenced by outliers
A. The process of feeding data into the network
B. The method of calculating the gradient of the loss function and updating the weights
C. The technique of training the network layer by layer
D. The strategy for initializing the network's weights
A. Sigmoid function
B. ReLU (Rectified Linear Unit)
C. Batch normalization
D. Weight initialization
A. Increasing the size of the dataset artificially
B. Normalizing or standardizing the data
C. Selecting a suitable machine learning algorithm
D. Simplifying the model architecture
