Reusing model fitted by cross_val_score in sklearn using joblib
<p>I created the following function in python:</p>

<pre><code>def cross_validate(algorithms, data, labels, cv=4, n_jobs=-1):
    print "Cross validation using: "
    for alg, predictors in algorithms:
        print alg
        print
        # Compute the accuracy score for all the cross validation folds. 
        scores = cross_val_score(alg, data, labels, cv=cv, n_jobs=n_jobs)
        # Take the mean of the scores (because we have one for each fold)
        print scores
        print("Cross validation mean score = " + str(scores.mean()))

        name = re.split('\(', str(alg))
        filename = str('%0.5f' %scores.mean()) + "_" + name[0] + ".pkl"
        # We might use this another time 
        joblib.dump(alg, filename, compress=1, cache_size=1e9)  
        filenameL.append(filename)
        try:
            move(filename, "pkl")
        except:
            os.remove(filename) 

        print 
    return
</code></pre>

<p>I thought that in order to do cross validation, sklearn had to fit your function.</p>

<p>However, when I try to use it later (f is the pkl file I saved above in <code>joblib.dump(alg, filename, compress=1, cache_size=1e9))</code>:</p>

<pre><code>alg = joblib.load(f)  
predictions = alg.predict_proba(train_data[predictors]).astype(float)
</code></pre>

<p>I get no error in the first line (so it looks like the load is working), but then it tells me <code>NotFittedError: Estimator not fitted, call</code>fit<code>before exploiting the model.</code> on the following line.</p>

<p>What am I doing wrong? Can't I reuse the model fitted to calculate the cross-validation? I looked at <a href="https://stackoverflow.com/questions/20483893/keep-the-fitted-parameters-when-using-a-cross-val-score-in-scikits-learn">Keep the fitted parameters when using a cross_val_score in scikits learn</a> but either I don't understand the answer, or it is not what I am looking for. What I want is to save the whole model with joblib so that I can the use it later without re-fitting.</p>