A classification pipeline is defined as a sequence of tasks that needs to be performed to classify the instances belonging to a given dataset into a set of predefined categories.TPOT- A Tree-based Pipeline Optimization Tool for Automating Machine LearningTPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data.
Once TPOT is finished searching (or you get tired of waiting), it provides you with the Python code for the best pipeline it found so you can tinker with the pipeline from there.Working with TPOTTo import TPOT: from tpot import TPOTClassifierCreate an instance of TPOT:pipeline_optimizer = TPOTClassifier()Custom TPOT parameters might look like:pipeline_optimizer = TPOTClassifier(generations=5, population_size=20, cv=5,random_state=42, verbosity=2)Now TPOT is ready to optimize a pipelinepipeline_optimizer.fit(X_train, y_train)Evaluate the final pipeline on the testing set with the score function:print(pipeline_optimizer.
score(X_test, y_test))Finally, to export the corresponding Python code for the optimized pipeline:pipeline_optimizer.export('tpot_exported_pipeline.py')The following code illustrates how TPOT can be employed for performing a regression task over the Boston housing prices dataset.from tpot import TPOTRegressorfrom sklearn.
datasets import load_bostonfrom sklearn.
model_selection import train_test_splithousing = load_boston()X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, train_size=0.75, test_size=0.25)tpot = TPOTRegressor(generations=5, population_size=50, verbosity=2)tpot.fit(X_train, y_train)print(tpot.score(X_test, y_test))tpot.export('tpot_boston_pipeline.py')Running this code should discover a pipeline (exported as tpot_boston_pipeline.
py) that achieves at least 10 mean squared error (MSE) on the test set:import numpy as npfrom sklearn.ensemble import GradientBoostingRegressorfrom sklearn.model_selection import train_test_split# NOTE: Make sure that the class is labeled 'class' in the data filetpot_data = np.
recfromcsv('PATH/TO/DATA/FILE',delimiter='COLUMN_SEPARATOR', dtype=np.float64)features = np.delete(tpot_data.view(np.float64).reshape(tpot_data.size, -1), tpot_data.dtype.names.index('class'), axis=1)training_features, testing_features, training_target, testing_target = train_test_split(features, tpot_data['class'], random_state=None)exported_pipeline = GradientBoostingRegressor(alpha=0.85, learning_rate=0.1, loss="ls", max_features=0.9, min_samples_leaf=5, min_samples_split=6)exported_pipeline.
fit(training_features, training_target)results = exported_pipeline.predict(testing_features)
What is the review of CIPET, Chennai?
Cipetoffers high quality technical consultancy and advisory service through its technology support services (TSS) .Tss is an intregal port folio ofcipetis highlights it's core competency by offering high quality service to customers in the area of tooling,precision Machine on CNC machines,design and Manufacturing of Moulds,tool and &dies for manufacturing plastic products,CAD/CAM/CAEservices,p plastic product manufacturing through state -of -the-art injection molding machines,blow molding,PET,stretch blow molding,pipe and film extrusion , standardization ,testing and quality control for plastic materials and products ,pre and post delivery inspection(PDI)of plastic product like PVC and PE pipes ,woven sacks,water storage tank,Micro -irrigation equipments,Engineered bamboo boards ,polymer based composite doors etc .Faculties are experienced and they also teach very wellOnly core companies are invited for recruitments toCIPETPlacement for the plastic engineering were good .
all the students were able to secure jobs but many opted for higher studyAll the campus ofCIPETbig enough.labs are equipped well with best of instrument,collage library is very good and student can also make use of it.other facilities like classroom,seminar hall and washroom are also goodThere are hostels available on-campus as well as off -campus ,Mess food is damn cheap,food is not vary taste .
Placement quite good.
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