Google Colab mounting drive

 

Deep Learning Hyperparameter Tuning example

 https://www.kaggle.com/jamesleslie/titanic-neural-network-for-beginners

titanic-neural-network-for-beginners  : 

Summary:

Create_model is the key concept in the whole algorithm.

def create_model(lyrs=[8], act='linear', pt='Adam', dr=0.0):

used GridsearchCV to find the best Hyperparameter Tuning.

Hyperparameters:  batch_size , epochs , optimizer , layers and drops 

Hyperparameter Tuning

Grid searchCV - batch size and epochs

batch_size = [16, 32, 64]

epochs = [50, 100]

Best: 0.822671 using {'batch_size': 32, 'epochs': 50}

===================================================

Grid searchCV - Optimization Algorithm

optimizer = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Nadam']

Best: 0.822671 using {'opt': 'Adam'}

===================================================

Grid searchCV - Hidden neurons

       layers = [[8],[10],[10,5],[12,6],[12,8,4]]

Best: 0.822671 using {'lyrs': [8]}

===================================================

Grid searchCV - Dropout

drops = [0.0, 0.01, 0.05, 0.1, 0.2, 0.5]

Best: 0.824916 using {'dr': 0.2}

===================================================

model = create_model(lyrs=[8], dr=0.2)

training = model.fit(X_train, y_train, epochs=50, batch_size=32, 

                     validation_split=0.2, verbose=0)

Still have few questions -- 

a. Initial train model given val_acc: 86.53% but where as train model at the end given acc: 83.16%

b. making batch size and epochs as constant values and then the remaining hyperparmaters found the best value

   rather than adding one hyperparameter and then another hyperparameter.

7 Steps of ML

 

Evolution of XGBoost Algorithm from Decision Trees

Credit to 

 https://towardsdatascience.com/https-medium-com-vishalmorde-xgboost-algorithm-long-she-may-rein-edd9f99be63d

XGBoost is a decision-tree-based ensemble Machine Learning algorithm that uses a gradient boosting framework. In prediction problems involving unstructured data (images, text, etc.) artificial neural networks tend to outperform all other algorithms or frameworks. However, when it comes to small-to-medium structured/tabular data, decision tree based algorithms are considered best-in-class right now.

The algorithm differentiates itself in the following ways:

1. A wide range of applications: Can be used to solve regression, classification, ranking, and user-defined prediction problems.
2. Portability: Runs smoothly on Windows, Linux, and OS X.
3. Languages: Supports all major programming languages including C++, Python, R, Java, Scala, and Julia.
4. Cloud Integration: Supports AWS, Azure, and Yarn clusters and works well with Flink, Spark, and other ecosystems.

Imagine that you are a hiring manager interviewing several candidates with excellent qualifications. Each step of the evolution of tree-based algorithms can be viewed as a version of the interview process.

1. Decision Tree: Every hiring manager has a set of criteria such as education level, number of years of experience, interview performance. A decision tree is analogous to a hiring manager interviewing candidates based on his or her own criteria.
2. Bagging: Now imagine instead of a single interviewer, now there is an interview panel where each interviewer has a vote. Bagging or bootstrap aggregating involves combining inputs from all interviewers for the final decision through a democratic voting process.
3. Random Forest: It is a bagging-based algorithm with a key difference wherein only a subset of features is selected at random. In other words, every interviewer will only test the interviewee on certain randomly selected qualifications (e.g. a technical interview for testing programming skills and a behavioral interview for evaluating non-technical skills).
4. Boosting: This is an alternative approach where each interviewer alters the evaluation criteria based on feedback from the previous interviewer. This ‘boosts’ the efficiency of the interview process by deploying a more dynamic evaluation process.
5. Gradient Boosting: A special case of boosting where errors are minimized by gradient descent algorithm e.g. the strategy consulting firms leverage by using case interviews to weed out less qualified candidates.
6. XGBoost: Think of XGBoost as gradient boosting on ‘steroids’ (well it is called ‘Extreme Gradient Boosting’ for a reason!). It is a perfect combination of software and hardware optimization techniques to yield superior results using less computing resources in the shortest amount of time.

System Optimization:

1. Parallelization: XGBoost approaches the process of sequential tree building using parallelized implementation. This is possible due to the interchangeable nature of loops used for building base learners; the outer loop that enumerates the leaf nodes of a tree, and the second inner loop that calculates the features. This nesting of loops limits parallelization because without completing the inner loop (more computationally demanding of the two), the outer loop cannot be started. Therefore, to improve run time, the order of loops is interchanged using initialization through a global scan of all instances and sorting using parallel threads. This switch improves algorithmic performance by offsetting any parallelization overheads in computation.
2. Tree Pruning: The stopping criterion for tree splitting within GBM framework is greedy in nature and depends on the negative loss criterion at the point of split. XGBoost uses ‘max_depth’ parameter as specified instead of criterion first, and starts pruning trees backward. This ‘depth-first’ approach improves computational performance significantly.
3. Hardware Optimization: This algorithm has been designed to make efficient use of hardware resources. This is accomplished by cache awareness by allocating internal buffers in each thread to store gradient statistics. Further enhancements such as ‘out-of-core’ computing optimize available disk space while handling big data-frames that do not fit into memory.

Algorithmic Enhancements:

1. Regularization: It penalizes more complex models through both LASSO (L1) and Ridge (L2) regularization to prevent overfitting.
2. Sparsity Awareness: XGBoost naturally admits sparse features for inputs by automatically ‘learning’ best missing value depending on training loss and handles different types of sparsity patterns in the data more efficiently.
3. Weighted Quantile Sketch: XGBoost employs the distributed weighted Quantile Sketch algorithm to effectively find the optimal split points among weighted datasets.
4. Cross-validation: The algorithm comes with built-in cross-validation method at each iteration, taking away the need to explicitly program this search and to specify the exact number of boosting iterations required in a single run.

Machine Learning Validation Techniques

Credit to 

 https://towardsdatascience.com/validating-your-machine-learning-model-25b4c8643fb7

The following methods for validation will be demonstrated:

• Train/test split
• k-Fold Cross-Validation
• Leave-one-out Cross-Validation
• Leave-one-group-out Cross-Validation
• Nested Cross-Validation
• Time-series Cross-Validation
• Wilcoxon signed-rank test
• McNemar’s test
• 5x2CV paired t-test
• 5x2CV combined F test