classification

问题 I'm trying to build a basic character recognition model using the many classifiers that scikit provides. The dataset being used is a standard handwritten set of alphanumeric samples (Chars74K image dataset taken from this source: EnglishHnd.tgz). There are 55 samples of each character (62 alphanumeric characters in all), each being 900x1200 pixels. I'm flattening the matrix (first converting to grayscale) into a 1x1080000 array (each representing a feature). for sample in sample_images: #

问题 I am using the following code (source) to concatenate multiple feature extraction methods. from sklearn.pipeline import Pipeline, FeatureUnion from sklearn.model_selection import GridSearchCV from sklearn.svm import SVC from sklearn.datasets import load_iris from sklearn.decomposition import PCA from sklearn.feature_selection import SelectKBest iris = load_iris() X, y = iris.data, iris.target pca = PCA(n_components=2) selection = SelectKBest(k=1) # Build estimator from PCA and Univariate

问题 Good day! I have a celebrity dataset on which I want to fine-tune a keras built-in model. SO far what I have explored and done, we remove the top layers of the original model (or preferably, pass the include_top=False) and add our own layers, and then train our newly added layers while keeping the previous layers frozen. This whole thing is pretty much like intuitive. Now what I require is, that my model learns to identify the celebrity faces, while also being able to detect all the other

问题 I can't figure out how to take a matrix and count the amount of the alphanumerical values for each row. I will only be taking in matrices with the values I'm counting. For example, if I got: ABA455 7L9O36G DZLFPEI I would get something like A:2 B:1 4:1 5:2 for the first row and each row would be counted independently. I would most like to understand the operators used if you could please explain them too. Thank you. 回答1: The following should work in any mainstream APL implementation. Let's

问题 I can't figure out how to take a matrix and count the amount of the alphanumerical values for each row. I will only be taking in matrices with the values I'm counting. For example, if I got: ABA455 7L9O36G DZLFPEI I would get something like A:2 B:1 4:1 5:2 for the first row and each row would be counted independently. I would most like to understand the operators used if you could please explain them too. Thank you. 回答1: The following should work in any mainstream APL implementation. Let's

问题 I have a basic decision tree classifier with Scikit-Learn: #Used to determine men from women based on height and shoe size from sklearn import tree #height and shoe size X = [[65,9],[67,7],[70,11],[62,6],[60,7],[72,13],[66,10],[67,7.5]] Y=["male","female","male","female","female","male","male","female"] #creating a decision tree clf = tree.DecisionTreeClassifier() #fitting the data to the tree clf.fit(X, Y) #predicting the gender based on a prediction prediction = clf.predict([68,9]) #print

问题 I have built a Keras model for extracting information from a raw input of text input. I am getting an accuracy of 0.9869. How can I know which of the training data is making the accuracy go low? I have pasted the code I am using below. import numpy as np from keras.models import Model, load_model from keras.layers import Input, Dense, LSTM, Activation, Bidirectional, Dot, Flatten from keras.callbacks import ModelCheckpoint x_nyha = np.load("data/x_nyha.npy") y_nyha = np.load("data/y/y_nyha

问题 I'm using GradientBoostingClassifier for my unbalanced labeled datasets. It seems like class weight doesn't exist as a parameter for this classifier in Sklearn. I see I can use sample_weight when fit but I cannot use it when I deal with VotingClassifier or GridSearch. Could someone help? 回答1: Currently there isn't a way to use class_weights for GB in sklearn. Don't confuse this with sample_weight Sample Weights change the loss function and your score that you're trying to optimize. This is

问题 I'm using GradientBoostingClassifier for my unbalanced labeled datasets. It seems like class weight doesn't exist as a parameter for this classifier in Sklearn. I see I can use sample_weight when fit but I cannot use it when I deal with VotingClassifier or GridSearch. Could someone help? 回答1: Currently there isn't a way to use class_weights for GB in sklearn. Don't confuse this with sample_weight Sample Weights change the loss function and your score that you're trying to optimize. This is

问题 I'm using GradientBoostingClassifier for my unbalanced labeled datasets. It seems like class weight doesn't exist as a parameter for this classifier in Sklearn. I see I can use sample_weight when fit but I cannot use it when I deal with VotingClassifier or GridSearch. Could someone help? 回答1: Currently there isn't a way to use class_weights for GB in sklearn. Don't confuse this with sample_weight Sample Weights change the loss function and your score that you're trying to optimize. This is